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Association between heavy metal exposure and bacterial vaginosis: A cross-sectional study
Bacterial vaginosis (BV) is a prevalent cause of vaginal symptoms in women of reproductive age. With the widespread of heavy metal pollutants and their harmful function on women’s immune and hormonal systems, it is necessary to explore the association between heavy metal exposure and BV. This study investigates the potential relationship between serum heavy metals and bacterial vaginosis in a cohort of American women. The present study employed a cross-sectional analysis of 2,493 women participating in the 2001–2004 National Health and Nutrition Examination Survey (NHANES). Multivariable logistic regression models were utilized in the study to assess the correlation between these variables. A stratified analysis was performed to investigate the relationship among different population groups further, and smooth curve fittings were conducted to intuitively evaluate the correlation. According to the current cross-sectional study results, a significant correlation was identified between the high levels of lead and cadmium in the serum and the likelihood of developing bacterial vaginosis. We found that serum lead (OR = 1.35, 95% CI: 1.06–1.72, p = 0.016) and serum cadmium (OR = 1.41, 95% CI: 1.01–1.98, p = 0.047) increased the risk of bacterial vaginosis by 35% and 41%, respectively, in the highest level group in comparison to the lowest level group in the fully adjusted model. Furthermore, the research discovered no statistically significant association between the levels of total mercury in the serum and a heightened susceptibility to bacterial vaginosis (OR = 0.96, 95% CI: 0.75–1.23, p = 0.763). Results of our study indicated an inverse association between serum heavy metals and bacterial vaginosis risk, including lead and cadmium. Reducing exposure to heavy metals could be vital to preventing and managing bacterial vaginosis.
Relationship between mental health and substance abuse on COVID-19 vaccine hesitancy in youth: A mixed methods longitudinal cohort study
Background Mental health and substance use challenges are highly correlated in youth and have been speculated to be associated with COVID-19 vaccine hesitancy. Literature has also suggested that mental health challenges in youth have increased during the COVID-19 pandemic. However, the longitudinal relationship between mental health challenges in youth and COVID-19 vaccine hesitancy is not well established. Objective We examined the relationship between mental health, substance use and COVID-19 vaccine hesitancy in youth during the COVID-19 pandemic. Methods Youth ages 14 to 29-years participated in a longitudinal survey study. Participants provided sociodemographic, mental health, and substance use data, as well as qualitative and quantitative information on their vaccine perspectives every two months between February 2021 to August 2021, and on February 2022. Generalized estimating equation logistic regression models were used to analyze the effect of mental health and substance use on vaccine hesitancy over time. Qualitative content area analyses were used to identify trends in vaccine attitudes. Results Mental health challenges and substance use frequency were associated with vaccine hesitancy, and significantly increased the odds of vaccine hesitancy over time. Additionally, mental health challenges were associated with decreases in vaccine hesitancy (OR: 0.80 (95% CI 0.66, 0.97)) when vaccines first began to emerge, but increases in vaccine hesitancy (OR: 1.72 (95% CI 1.32, 2.26)) one year later. Participants reported perceptions regarding vaccine safety and efficacy were the primary determinants influencing hesitant, uncertain, and acceptant vaccine attitudes. Additionally, changes in vaccine attitudes over time for some participants, were associated with changes in mental health. Conclusions Increases in mental health challenges and substance use were associated with increases in COVID-19 vaccine hesitancy in youth over the COVID-19 pandemic. Health policy agencies should be aware of the potential impact of mental health challenges and substance use in youth, when developing vaccine policy and programs.
Comparison of early warning scoring systems for predicting stroke occurrence among hospitalized patients: A study using smart clinical data warehouse
Background This study aimed to evaluate the predictive ability of two widely used early warning scoring systems, the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS), for predicting stroke occurrence in hospitalized patients. Methods The study enrolled 5,474 patients admitted to the intensive care unit from the general ward using data from the Smart Clinical Data Warehouse (CDW). MEWS and NEWS were calculated based on vital signs and clinical parameters within four hours of stroke onset. Stroke occurrence was categorized as ischemic or hemorrhagic. Logistic regression and receiver operating characteristic curve analyses were performed to assess the predictive abilities of the scoring systems. Results Of the enrolled patients, 33.9% (n = 1853) experienced stroke, comprising 783 cases of ischemic stroke and 1,070 cases of hemorrhagic stroke. Both the MEWS and the NEWS were found to significantly predict overall stroke occurrence with a cutoff value of 4 (MEWS>4; OR [95% CI]: 13.90 [11.51–16.79], p<0.001; NEWS>4; OR [95% CI]: 6.71 [5.75–7.83], p<0.001). Parameters, such as prior malignancy, atrial fibrillation, AVPU response, heart rate, respiratory rate, and oxygen saturation, are also associated with stroke occurrence. The predictive ability of MEWS and NEWS was good for overall stroke occurrence. (AUC of MEWS: 0.92, 95% CI [0.91–0.93], p<0.001; AUC of NEWS: 0.85, 95% CI [0.84–0.86], p<0.001). The predictive ability was considered fair for ischemic stroke but good for hemorrhagic stroke. Conclusion MEWS and NEWS demonstrated significant predictive abilities for overall stroke occurrence among hospitalized patients, with MEWS slightly outperforming NEWS.
“They are not HIV treatments drugs; they are preventive drugs (PrEP)”. Experiences of PrEP uptake among vulnerable adolescent girls and young women in Tanzania
Introduction HIV poses a significant global health concern, affecting adolescents among other populations. This is attributed to various vulnerabilities including biological factors, gender inequalities and limited access to comprehensive sexual and reproductive health services in sub-Saharan Africa. In Tanzania, adolescent girls, and young women (AGYW) face double the risk of HIV infection compared to their male counterparts. The introduction of pre-exposure prophylaxis (PrEP) in early 2018 brought hope for changing the HIV cascade in the country. However, numerous challenges still hinder PrEP uptake. Therefore, this study explored experiences of PrEP uptake among vulnerable AGYW in Tanzania. Methods This study employed a phenomenological qualitative approach; 52 semi-structured interviews were carried out between May to November 2022 in the selected healthcare facilities in Tanzania. The study adopted inductive-deductive thematic analysis guided by the Social Ecological Model (SEM) to elicit the views of AGYW aged 15–24. Nvivo software was utilised to organise data. Results This study has uplifted barriers and facilitators on PrEP uptake among AGYW in Tanzania. The barriers are categorized at individual, interpersonal, and institutional levels. The individual level barriers included pre-requisites for initiating PrEP, disbelief in the effectiveness of PrEP, interference of refill hours with working hours, financial constraints, and adherence to the pills. The interpersonal level barriers included misconceptions about PrEP pills, and labelling of PrEP users. The institutional level barriers included inadequate privacy, PrEP drug stockout, being turned away by health care facilities (HCF), long waiting times, and distance to the HCF. Facilitators included factors at individual level (experienced benefit of PrEP, adequate PrEP knowledge, having multiple partners, perceived risk due to the nature of the work, PrEP ensuring privacy), interpersonal level (support from social networks), and institutional level (Free availability of PrEP, receiving refill reminders). Conclusions To overcome barriers to PrEP uptake among AGYW, it is crucial to develop multi-level interventions that consider personal, social, and structural factors hindering PrEP uptake. Implementing strategies like prioritizing off-site PrEP delivery and expanding community outreach for PrEP awareness can help dispel misconceptions and enhance uptake.
Traditional online and live-streaming dual-channel strategies and pricing policies
This study examines the pricing policies and the dual-channel strategies in a two-tier supply chain where a manufacturer distributes products through a retailer and considers the opening of a traditional direct online channel or a live-streaming sales channel The manufacturer has three sales modes, i.e., traditional retail channel only, opening a traditional direct online channel and opening a live-streaming direct sales channel, to consider. The manufacturer and the retailer play a Stackelberg game wherein the manufacturer has the first mover advantage. The two supply chain members can follow two decision sequences where the manufacturer decides the direct online retail price before the retailer does in one, and the manufacturer and the retailer cooperate to decide the retail prices simultaneously in the other. The profit optimization models for the three sales modes are formulated, and the closed forms of the optimal solutions are then derived through backward induction. The optimal pricing decisions and profits with different decision sequences are compared and analyzed among the three sales modes. The results suggest that the manufacturer should introduce a live-streaming direct sales channel when facing fierce competition, and should not introduce a dual channel to create a win-win situation for the two supply chain members when facing mild competition. The manufacturer should increase the wholesale and the direct online retail prices and make more live-streaming sales effort, and the retailer should also increase the retail price, if the streamer has good sales skills when introducing a live-streaming sales channel. The manufacturer and the retailer should decide the retail prices simultaneously when a live-streaming sale channel is introduced.
Two-stage Non-Intrusive Load Monitoring method for multi-state loads
The loads that have several working states cannot be accurately distinguished by the conventional Non-Intrusive Load Monitoring (NILM) methods. This paper proposed an improved NILM method based on the Resnet18 Convolutional Neural Network (CNN) and Support Vector Machine (SVM) algorithm to address the misidentification of multi-state appliances. The V-I trajectories of loads are at first classified with Resnet18. Then, load features with low redundancy is obtained through the Max-Relevance and Min-Redundancy (mRMR) feature selection algorithm from various operating states of loads that were not successfully classified. The SVM algorithm is developed for two-stage identification to achieve high accuracy of classification for identifying the multi-state appliances quickly. This proposed NILM method can significantly improve the accuracy of identification for multi-state loads. Finally, the Plaid dataset is acquired to validate the effectiveness and accuracy of the proposed method.
Correction: Impact of duck stocking density on growth performance, digestive enzymes, blood biochemistry, and antioxidant capacity of the Labeo rohita reared in an integrated ponds system
Insignificant difference in medication adherence to dyslipidemia drugs between visually impaired and non-disabled people in South Korea: A nationwide cohort study using claims records
Incidence of visual impairment (VI) and dyslipidemia is increasing with aging. Although good medication adherence (MA) is a crucial factor in achieving therapeutic goals for dyslipidemia, there is a paucity of studies measuring MA in the visually impaired with dyslipidemia. We investigated whether patients with VI had worse MA to dyslipidemia drugs than non-disabled people and determined the factors affecting MA among patients with VI. Data on dyslipidemia patients with VI were extracted in 2017 from the sample cohort database of the National Health Insurance Service. MA to dyslipidemia drugs was measured for two years based on the proportion of days covered (PDC). Conditional logistic regression analysis was performed to analyze the effect of VI on good MA (PDC ≥0.8). The VI group (0.860) had a larger PDC than the non-disabled group (0.850). The adjusted odds ratio (aOR) for good MA among VI vs. non-disabled individuals was statistically insignificant (1.137, 95% confidence interval:0.958–1.350). Significant factors for poor MA in the VI group were younger age (aOR for 20–39 vs. ≥75 years old: 0.124), lower income (aOR for 9-10th decile (rich) vs. 1-4th decile (poor): 1.771), shorter duration of dyslipidemia (aOR for 1–4 vs. 15 years: 0.416), having lower-level providers sas their main providers (aOR for clinics vs. general/tertiary-care hospitals: 0.545), and having mental diseases (aOR: 0.679). Patients with VI did not have worse MA than non-disabled patients taking dyslipidemia medication.
Tariffs, transportation, and profits in cross-border e-commerce: A dual-channel supply chain decision-making strategy
The development of cross-border e-commerce platform promotes the new channel model between domestic and international. How to determine the dual-channel pricing decision of manufacturers and retailers under the condition of tariff and transportation heterogeneity has become an important and realistic problem. Based on the perspective of cross-border e-commerce dual-channel supply chain, this paper considers the impact of import tariff, transport heterogeneity and export tax rebate, compares and analyzes the performance difference between decentralized decision-making and centralized decision-making, and analyzes the impact of import tariff, export tax rebate and transport heterogeneity on cross-border e-commerce dual-channel pricing, demand and profit. The results show that the tariff is positively correlated with the manufacturer’s direct selling price and the retailer’s retail price, while the tariff is negatively correlated with the wholesale price, the demand and profit of direct selling channel and the retail channel. Export tax rebate rate is positively correlated with manufacturers’ demand and profit and retailers’ demand and profit, and negatively correlated with manufacturers’ wholesale price, direct selling price and retail price. The increase of unit freight in direct channel is unfavorable to manufacturers and beneficial to retailers; The increase in unit freight rates in retail channels is bad for both manufacturers and retailers. Centralized decision-making is beneficial to supply chain demand and profits, and can improve the overall performance of the supply chain.
tomoseqr: A Bioconductor package for spatial reconstruction and visualization of 3D gene expression patterns based on RNA tomography
RNA tomography computationally reconstructs 3D spatial gene expression patterns genome-widely from 1D tomo-seq data, generated by RNA sequencing of cryosection samples along three orthogonal axes. We developed tomoseqr, an R package designed for RNA tomography analysis of tomo-seq data, to reconstruct and visualize 3D gene expression patterns through user-friendly graphical interfaces. We show the effectiveness of tomoseqr using simulated and real tomo-seq data, validating its utility for researchers. R package tomoseqr is available on Bioconductor (https://doi.org/doi:10.18129/B9.bioc.tomoseqr) and GitHub (https://github.com/bioinfo-tsukuba/tomoseqr).
An efficient smart phone application for wheat crop diseases detection using advanced machine learning
Globally, agriculture holds significant importance for human food, economic activities, and employment opportunities. Wheat stands out as the most cultivated crop in the farming sector; however, its annual production faces considerable challenges from various diseases. Timely and accurate identification of these wheat plant diseases is crucial to mitigate damage and enhance overall yield. Pakistan stands among the leading crop producers due to favorable weather and rich soil for production. However, traditional agricultural practices persist, and there is insufficient emphasis on leveraging technology. A significant challenge faced by the agriculture sector, particularly in countries like Pakistan, is the untimely and inefficient diagnosis of crop diseases. Existing methods for disease identification often result in inaccuracies and inefficiencies, leading to reduced productivity. This study proposes an efficient application for wheat crop disease diagnosis, adaptable for both mobile devices and computer systems as the primary decision-making engine. The application utilizes sophisticated machine learning techniques, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, combined with feature extraction methods such as Count Vectorization (CV) and Term Frequency-Inverse Document Frequency (TF-IDF). These advanced methods collectively achieve up to 99% accuracy in diagnosing 14 key wheat diseases, representing a significant improvement over traditional approaches. The application provides a practical decision-making tool for farmers and agricultural experts in Pakistan, offering precise disease diagnostics and management recommendations. By integrating these cutting-edge techniques, the system advances agricultural technology, enhancing disease detection and supporting increased wheat production, thus contributing valuable innovations to both the field of machine learning and agricultural practices.
Modeling nearshore-offshore water exchange in Lake Ontario
The water quality and resources of Lake Ontario’s nearshore ecosystem undergo heightened stress, particularly along the northwest shoreline. Hydrodynamic processes linking the distinct nearshore and offshore trophic structures play a crucial role in transporting nutrient-loaded water along and across the shore. Despite the pivotal connection between algae growth and the development of nuisance proportions, the scales over which these processes operate remain poorly understood. This study delves into the exchange dynamics between nearshore and offshore areas of Lake Ontario throughout 2018, employing a validated three-dimensional numerical model. A virtual passive age tracer is utilized to discern horizontal mixing time scales between nearshore regions of the lake (water depth < 30 m) and offshore locations. The dispersal pattern, as revealed by a passive tracer released from eight points around the model lake’s perimeter, indicates more extensive diffusion in late summer when lake-wide stratification is established, compared to the mixed period. Coastal upwelling events, leading to intrusions of hypolimnetic waters, significantly contribute to net cross-shore transport, with the most pronounced effects observed in May and June when the offshore thermocline is shallow. In the northern part of the lake, dispersal predominantly occurs alongshore, mirroring the prevailing cyclonic (counterclockwise) coastal circulation during the stratified season. This pattern is a consequence of a 45% increase in upwelling events compared to three decades ago. In the northwestern and southern sectors of the lake, elevated cross-shore mixing is attributed to geomorphology-induced cross-basin currents.
Denovo variants in POGZ and YY1 genes: The novel mega players for neurodevelopmental syndromes in two unrelated consanguineous families
Novel denovo variants of exome sequences are major cause of pathogenic neurodevelopmental disorders with a dominant genetic mechanism that emphasize their heterogeneity and complex phenotypes. White Sutton syndrome and Gabriele-de-Vries syndrome are congenital neuro-impairments with overlap of severe intellectual disability, microcephaly, convulsions, seizures, delayed development, dysmorphism of faces, retinal diseases, movement disorders and autistic traits. POGZ gene codes for pogo transposable element-derived zinc-finger protein and YY1 gene regulates transcription, chromatin, and RNA-binding proteins that have been associated with White Sutton and Gabriele-de-Vries syndromes, in recent data. We present probands of two unrelated consanguineous families with complicated, unexplained neurocognitive syndromic characteristics clinically undiagnosed. Objectives of the study were to identify altered genetics and protein characteristics underlying molecular pathological pathways in both the patients. Whole exome sequencing identifies novel, denovo missense variant NM_015100.4: c.776 C>T (p. Pro259Leu) in exons 19 of POGZ gene and non-frameshift variant NM_003403.5: c.141_143delGGA (p. Glu47del) in exon 1 of YY1 gene for White Sutton syndrome in eight years five-month-old girl and Gabriele-de-Vries syndrome in seven years eight months old boy residing in Rawalpindi and Chakwal districts of Punjab, Pakistan respectively. Protein modelling for identified variants predicts size and conformation modifications in mutated amino acid residues that lead to damaging effects in the conserved domains expressed as neurological pathophysiology. The present study widens the diversely ethnic and highly inbred gene pool of Punjab, Pakistan population for spontaneously originated deleterious mutations and contributes to the continuously expanding phenotypic canvas. Molecular genetic identification and personalized diagnosis for the patients suffering from complicated neurodevelopmental phenotypes, for better care, management of day-to-day activities and prolonged life span are the utmost hopes.
Phenotypic identification of Metallo-ß- lactamase resistance Gram negative bacteria from a clinical specimen in Sidama, Ethiopia
Background Metallo-beta lactamase resistance is one of the carbapenem resistances that worsen the world nowadays. A new variant of carbapenem-resistant has only limited reports from Africa including Ethiopia. This study aimed to determine Metallo -ß- lactamase resistance Gram-negative bacteria in Hawassa University Comprehensive Specialized Hospital January–June 2023. Method A cross-sectional study was conducted in which consecutive patients infected with Gram-negative bacteria were included in the study. A structured questionnaire was used to collect the data with oriented nurses if the patients/or caregivers gave consent to participate in the study. Clinical specimens are processed based on the standard operating procedure of the Microbiology laboratory and Clinical laboratory standard institute guidelines. Culture and sensitivity testing was used to isolate the bacteria. Gram staining and biochemical tests was used to identify the bacteria to genus and species. Kirby disc diffusion technique was used to determine the susceptibility of antibiotics. Statistical Software for Social Science (SPSS) version 21 is used for data entry and analysis. Descriptive statistics and logistic regression were used to interpret the data. The odds ratio at 95% confidence interval (CI) and p-value < 0.05 were taken as a statistically significant association. Result Our study included 153 isolates from different specimens, 83 (54.2%) were from male patients and 70 (45.8%) were from females. Klebsiella pneumonia was the predominant 43, followed by Escherichia coli 32, Acinetobacter spp 25, Pseudomonas spp 15, Enterobacter agglomerus 9, Klebsiella ozaenae 6, Enterobacter cloacae 5, Klebsiella oxytoca 4, (Klebsiella rhinoscleromatis, Proteus mirabilis and Morganella morganii) 3, Providencia stuartii 2 and (Citrobacter spp & Proteus vulgaris) 1. The rates of multi, extensive and pan-drug resistance bacteria accounted for 128/153 (83.7%), 77 /153(50.3%), and 26/153 (17.0%), respectively. Carbapenem resistance was 21 (13.7%), of this 7.2% were Enterobacteriaceae, 5.2% were Acetinobacter spp. and 1.3% Pseudomonas spp. Metallo-beta-lactamase was 17 (11.1%), of this, Enterobacteriaceae were 9(5.9%), Acetinobacter spp. 7(4.6%), and Pseudomonas spp. 1(0.7%). There were no variables statistically significantly associated with metallo-beta-lactamase-resistant. Conclusion Our study revealed that Metallo-beta-lactamase resistance was circulating in the study area. There was a high rate of carbapenem resistance, multi, extensive and pan-drug resistance. Therefore, a measure should be taken to alleviate the emerging threat that leaves the patients without the option of treatment.
Differences in gene expression between high and low tolerance rainbow trout (Oncorhynchus mykiss) to acute thermal stress
Understanding the mechanisms that underlie the adaptive response of ectotherms to rising temperatures is key to mitigate the effects of climate change. We assessed the molecular and physiological processes that differentiate between rainbow trout (Oncorhynchus mykiss) with high and low tolerance to acute thermal stress. To achieve our goal, we used a critical thermal maximum trial in two strains of rainbow trout to elicit loss of equilibrium responses to identify high and low tolerance fish. We then compared the hepatic transcriptome profiles of high and low tolerance fish relative to untreated controls common to both strains to uncover patterns of differential gene expression and to gain a broad perspective on the interacting gene pathways and functional processes involved. We observed some of the classic responses to increased temperature (e.g., induction of heat shock proteins) but these responses were not the defining factors that differentiated high and low tolerance fish. Instead, high tolerance fish appeared to suppress growth-related functions, enhance certain autophagy components, better regulate neurodegenerative processes, and enhance stress-related protein synthesis, specifically spliceosomal complex activities, mRNA regulation, and protein processing through post-translational processes, relative to low tolerance fish. In contrast, low tolerance fish had higher transcript diversity and demonstrated elevated developmental, cytoskeletal, and morphogenic, as well as lipid and carbohydrate metabolic processes, relative to high tolerance fish. Our results suggest that high tolerance fish engaged in processes that supported the prevention of further damage by enhancing repair pathways, whereas low tolerance fish were more focused on replacing damaged cells and their structures.
Hospital and physician-based mental healthcare during 12 months of opioid agonist treatment for opioid use disorder: Exploring costs and factors associated with acute care
Background Individuals with opioid use disorder (OUD) have a high prevalence of co-occurring mental health disorders; however, there exists little information on mental health service use for this population. We aimed to determine the prevalence of non-substance use-related mental health emergency department (ED) visits, hospitalizations, and outpatient physician visits for individuals receiving treatment for OUD over one year. We also explored individual-level characteristics associated with mental health care service use and estimated the costs of this care. Methods We linked observational cohort data collected from 3,430 individuals receiving treatment for OUD in Ontario, Canada, with health administrative records available for all individuals enrolled in Ontario’s public health insurance program. Eligible participants were receiving medication treatment for OUD and were recruited between 2011 and 2021 Starting on the day of cohort enrolment, we included health service data for up to 12 months. We identified ED visits and hospitalizations for non-substance use-related mental health disorders using ICD-10-CA diagnostic codes. Outpatient mental health visits to primary care providers and psychiatrists were ascertained by examining the diagnostic codes of physician billing claims. We used logistic regression to explore the association between demographic and clinical factors of interest and mental health-related ED visits or hospitalizations. Mean one-year mental healthcare costs, calculated in 2022 Canadian dollars, were estimated. We fit a two-part zero-inflated negative binomial model to explore the association between factors of interest and healthcare costs. Findings Altogether, 14.9% of individuals had mental health-related acute care ED visits or hospitalizations and 37.3% had outpatient mental health visits during the follow up period. For participants with at least one visit, we determined the mean number of ED visits (1.93, standard deviation [SD] = 2.15), hospitalizations (1.46, SD = 1.05), primary care visits (3.51, SD = 4.31), and psychiatry visits (4.04, SD = 4.73). Lower odds of ED use and hospitalization were associated with older age (46+ compared to less than 25 years: odds ratio [OR] 0.43, 95% confidence interval [CI]: 0.29, 0.63) and being employed (OR 0.48, 95% CI 0.37, 0.61). Higher odds of ED use and hospitalization was associated with positive opioid urine drug screens (50% positive urine drug screens compared to 0%: OR 1.45, 95% CI 1.05, 2.01), having more comorbid conditions (7+ health conditions compared to 0–2 health conditions: OR 3.76, 95% CI 2.60, 5.44), and receipt of outpatient mental healthcare (OR 2.38, 95% CI 1.95, 2.92) were associated with higher odds of ED visits or hospitalizations. Mean one-year mental healthcare costs for individuals receiving ED visits or hospitalizations totaled $9,117.80 (95% CI 7,372.90, 10,862.70) per person. Mean one-year costs for individuals with outpatient mental healthcare alone totaled $382.30 (95% CI 343.20, 421.30) per person. Conclusions Individuals receiving treatment for OUD receive care in EDs, inpatient units, and outpatient clinics for mental health conditions other than substance use-related diagnoses. Healthcare costs were considerably higher for those receiving acute care treatment for mental health conditions. Studying integrated mental health and substance use disorder treatment in the outpatient setting should be a priority to bolster care for this population.
SEMdag: Fast learning of Directed Acyclic Graphs via node or layer ordering
A Directed Acyclic Graph (DAG) offers an easy approach to define causal structures among gathered nodes: causal linkages are represented by arrows between the variables, leading from cause to effect. Recently, industry and academics have paid close attention to DAG structure learning from observable data, and many techniques have been put out to address the problem. We provide a two-step approach, named SEMdag(), that can be used to quickly learn high-dimensional linear SEMs. It is included in the R package SEMgraph and employs a two-stage order-based search using previous knowledge (Knowledge-based, KB) or data-driven method (Bottom-up, BU), under the premise that a linear SEM with equal variance error terms is assumed. We evaluated our framework’s for finding plausible DAGs against six well-known causal discovery techniques (ARGES, GES, PC, LiNGAM, CAM, NOTEARS). We conducted a series of experiments using observed expression (or RNA-seq) data, taking into account a pair of training and testing datasets for four distinct diseases: Amyotrophic Lateral Sclerosis (ALS), Breast cancer (BRCA), Coronavirus disease (COVID-19) and ST-elevation myocardial infarction (STEMI). The results show that the SEMdag() procedure can recover a graph structure with good disease prediction performance evaluated by a conventional supervised learning algorithm (RF): in the scenario where the initial graph is sparse, the BU approach may be a better choice than the KB one; in the case where the graph is denser, both BU an KB report high performance, with highest score for KB approach based on topological layers. Besides its superior disease predictive performance compared to previous research, SEMdag() offers the user the flexibility to define distinct structure learning algorithms and can handle high dimensional issues with less computing load. SEMdag() function is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.
The hidden drivers: Unraveling the impact of density, moisture, and scale on Hermetia illucens rearing
The black soldier fly (Hermetia illucens) is a saprophagous insect known for bioconverting organic waste, potentially offering environmental benefits, such as contributing to waste reduction and nutrient cycling. The performance of larvae varies significantly with factors substrate moisture, larval density, and scale of production. Three experiments were conducted using a mix of spent mushroom substrate (SMS) and chicken feed (CF). In the first experiment, 250 larvae were reared on 100 g dry matter (DM) feed at moisture levels of 65–75%. Results showed that the average individual larval weight, total biomass, and feed conversion ratio (FCR) improved with increased moisture. In the second experiment, 300 and 350 larvae/box were tested at 70% and 75% moisture. The highest average individual larval fresh weight (158.6 mg) was observed at 70% moisture with 250 larvae, while the highest biomass was achieved at 75% moisture with 300 larvae. Finally, different scales (10–2,500 g feed with 25–6,500 larvae) were tested with a similar feeding rate. The highest individual larval weight was recorded at the 100 g scale, with no clear correlation between weight and scale. However, the 50 g scale achieved the highest substrate reduction (33.2%). Overall, this study underscores the need to adjust moisture, density, and scale to nutrient conversion efficiency when using SMS, CF or other diets. The optimal results for the SMS feed mix were observed at 75% substrate moisture, 250 larvae per 100 g DM, and at approximately 2 larvae per cm2.
HEalth professionals Responding to MEn for Safety (HERMES): Mixed methods evaluation of a pilot sexual health intervention for gay, bisexual and other men who have sex with men experiencing domestic violence and abuse
Background Domestic violence and abuse (DVA) is a violation of human rights that damages the health and well-being of—gay, bisexual and other men who have sex with men (gbMSM). Sexual health services provide a unique opportunity to assess for DVA and provide support. This study explores the feasibility and acceptability of Healthcare Responding to Men for Safety (HERMES), a pilot intervention aimed to improve the identification and referral of gbMSM experiencing DVA in a London NHS Trust. Methods The before and after mixed method evaluation of the intervention included semi-structured interviews with 21 sexual health practitioners, 20 matched pre-post questionnaires, and an audit of 533 patient records to assess identification and referral of gbMSM experiencing DVA. Results HERMES increased practitioners’ self-reported preparedness and confidence in enquiring, identifying and responding to gbMSM experiencing DVA. HERMES increased staff awareness of DVA among these patients, which led to higher identification practices in their work. There was a significant increase in the identification and reporting practices of trained staff (0% to 30%), with 6 (5%) DVA cases identified. However, as far as we could determine, none of these patients contacted the support agency. Conclusions HERMES proved successful in raising staff awareness, provided tools that increased identification and a referral pathway to an external specialist DVA service for the LGBT community. However, the poor uptake of the referral service indicates a need for further exploration of the help-seeking behaviour of gbMSM experiencing DVA and whether they would prefer to receive support within a sexual health service. Reinforcement training and clinical supervision is needed to sustain positive changes in practice over time and address potential challenges posed by staff turnover. Initial training should be conducted through face-to-face sessions with a combination of in-person and e-learning materials and followed by in-person and online reinforcement activities.