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“All I want is to be involved in my treatment”: Experiences of HIV treatment and viral load monitoring among adolescents, young adults and healthcare providers in Tanzania

Background Virologic response is the earliest indicator of HIV treatment, making it the gold standard in monitoring treatment response. This study explored experiences and challenges of HIV treatment and viral load (HVL) monitoring among adolescents and young adults (AYA), parents or guardians of young adolescents and healthcare providers (HCPs). Methods This was a qualitative study conducted between March and May 2022 in five health facilities in an urban setting of Tanzania. We performed 20 in-depth interviews (IDIs) with AYA who had full HIV-positive status disclosure, 8 IDIs with parents or guardians of young HIV-infected adolescents, and five focus group discussions (FGDs) with 30 HCPs. The IDIs and FGD transcripts were translated from Swahili into English, transcribed, coded, and performed thematic analysis using NVivo software. Results AYA demonstrated an understanding of HIV treatment as lifelong and, most knew the benefits of HIV viral load (HVL) monitoring. It was apparent that 60% of older adolescents (15–19 years), parents or guardians, and young adults (20–24 years) discussed HVL results with HCPs and, the majority desire to be involved in their HIV treatment. HCPs reported that missing clinic appointments among AYA attending boarding schools or college contribute to delays in HVL testing and initiation of enhanced adherence counselling (EAC). Notably, HCP faced challenges recalling AYA who received three to six multi-month anti-retroviral drug (ARV) refills when their HVL results were high (VL ≥ 1000 copies/ml). Conclusions More than half of AYA expressed ownership in their HIV treatment; they discussed the meaning of their HVL results with HCPs. Additionally, AYA reported non-frequent challenges in HVL testing however, HCPs described missing appointments for AYA on three to six multi-months dispensing of ARVs as major challenges for implementing HVL testing. Importantly multi-months ARV dispensing in AYA needs program evaluation to inform the best implementation modality to support viral suppression.

Correction: Forecasting outbound student mobility: A machine learning approach

A deep learning based approach for classifying the maturity of cashew apples

Over 95% of cashew apples are left to waste and rot on the ground. However, both cashew nuts and the often overlooked cashew apples possess significant nutritional and economic value. The cashew apple constitutes the major part (90%) of the cashew fruit, with the nut forming a modest portion (10%). Cashew nuts can be harvested and processed even after lying on the ground, but cashew apples are more delicate. Assessing the maturity status of these apples still requires human visual observation due to the challenges posed by their moisture content. Timely harvesting is crucial, as the pseudofruit is prone to microbial infections upon hitting the ground, making the process time- and labor-intensive. In this study, a Deep Learning based image classification model is presented, which can be used to automatically identify mature cashew apples. The model achieved an accuracy of 95.58% in classifying the cashew apples (immature vs. mature). Overall, the results highlight the potential of Deep Learning models for the classification of cashew apples and other fruits for precision agriculture purposes. This approach could enhance the harvesting process by enabling the utilization of the entire fruit and reducing the need for manual labor, thereby unlocking the full economic potential of the cashew tree.

Continuum of maternal health care utilisation in Sub-Saharan African countries: A positive deviance approach

Introduction Maternal health is a global priority for achieving Sustainable Development Goal 3.1. However, many mothers in Africa still lack access to the full continuum of care for maternal health (continuum of care). The lowest coverage is often observed among underserved mothers, particularly those who are uneducated and from poor households. Despite these disadvantages, some mothers in every community find effective ways to access a better continuum of care – these are known as positive deviants. However, there is limited evidence to support this concept. Therefore, this study examined the determinants of continuum of health care utilisation among underserved mothers in sub-Saharan African countries. Methods Data from the Demographic and Health Surveys of 15 Sub-Saharan African countries with high maternal mortality ratio were used. A positive deviance approach was applied to identify 32,778 underserved mothers using a two-stage stratified sampling technique for the final analysis. A multilevel mixed-effect binary logistic regression analysis was conducted to identify factors associated with being a positive deviant. Finally, an adjusted odds ratio (AOR) with a 95% confidence interval (CI) was used to declare statistically significant determinants. Results The overall continuum of care utilisation among underserved mothers was 13.8% (95% CI: 13.5–14.2%). Underserved mothers who were employed (AOR = 1.2; 95% CI: 1.1–1.3), those who had educated husband (AOR = 1.3; 95% CI:1.2–1.4), had one to three children (AOR = 1.2; 95%CI: 1.1–1.3), had a history of pregnancy termination (AOR = 1.3; 95%CI: 1.1–1.4), had the healthcare decision making autonomy (AOR = 1.2; 95% CI: 1.1–1.3), and faced no barriers to accessing health services (AOR = 1.2; 95% CI: 1.0–1.2) were significantly associated with being a positive deviant. Conclusion Despite socioeconomic disadvantage, a subset of underserved mothers in sub-Saharan Africa successfully utilised the full continuum of maternal healthcare. Key enabling factors included maternal employment, partner education, smaller family size, prior pregnancy termination, autonomy in healthcare decision-making, and absence of access barriers. These findings support the positive deviance approach as a valuable lens for identifying actionable pathways to improve maternal health coverage. Interventions that amplify these enabling factors could help close equity gaps and accelerate progress toward maternal health targets in high-burden settings.

Dinitrogen Activation and Cyanide Release by the Gas-Phase Cluster Anions InNbC₂^–: Synergistic Effects of the p-Block Indium and the d-Block Niobium

Assessment of the genetic diversity of Atlantic bottlenose dolphin (Tursiops truncatus) strandings in the Mississippi Sound (USA)

The common bottlenose dolphin ( Tursiops truncatus ) is a key marine mammal species in the Gulf of Mexico, playing an essential role as a top predator. This study investigates the genetic diversity and population structure of bottlenose dolphins stranded in the Mississippi Sound from 2010 to 2021. Tissue samples (muscle, liver, lung, kidney, and brain) were collected from 511 stranded dolphins, and mitochondrial DNAs (mtDNA) were extracted for analysis. A total of 417 samples were successfully amplified and sequenced using high throughput sequencing, yielding 386 complete mitogenomes. Genetic diversity metrics, such as nucleotide and haplotype diversity, were calculated, and population structure was inferred for both mitochondrial control region (mtCR) and whole mitogenome sequences. Using the whole mitogenome, the study identified four genetically distinct populations within the Mississippi Sound, demonstrating regional variation in dolphin populations. Notably, two stranded individuals likely originated from populations outside the sampled area. The use of whole mitogenomes allowed for improved resolution of genetic diversity and population differentiation compared to previous studies using partial mtDNA sequences. These findings enhance our understanding of bottlenose dolphin population structure in the region and underscore the value of stranded animals for population genetic studies.

Perceptions of women and their partners on postabortion intrauterine contraception: A qualitative study in central Uganda

Background The uptake of intrauterine devices (IUDs) has stalled below two percent among married Ugandan women. We explored the perceptions of women and their partners on the utilization of postabortion IUDs after medical management of incomplete abortions in central Uganda. Methods Between August 2022 and May 2023, using a semi-structured interview guide, fifteen in-depth interviews were conducted among Ugandan women and their partners at five public facilities on their perceptions in regards to postabortion IUDs. Using inductive content analysis, themes and subthemes were generated. Results Three themes emerged: 1) perceived women’s and their partners’ barriers in accessing postabortion IUDs such as myths and misconceptions on IUDs, spouse refusal, IUD related side effects. 2) women’s and their partners’ experiences while using postabortion IUDs such as increased lubrication, freedom from prior contraceptive side effects, assurance of early return to fertility after IUD removal, menstrual irregularities and abdominal pain following IUD insertion. 3) motivators and recommendations to the uptake of IUDs such as peer influence, client-healthcare provider relationship, spousal approval of IUDs and community sensitization on IUDs using social media platforms. Conclusion Understanding the socio-cultural context of women and their partners, is pivotal in the uptake of postabortion IUDs. Healthcare providers ought to provide evidence-based counselling to demystify individual and community misconceptions on IUD use. Male partner involvement, the assurance of early return to fertility after IUD removal, user champions, and social media platforms can enhance the uptake of postabortion IUDs.

Pressure-Induced Dome-Like Superconductivity and Unusual Charge-Density-Wave-Like Transition in Th₂Cu₄As₅

The opportunity to save a life: A qualitative study of a point-of-care overdose education and naloxone distribution intervention

Canada’s opioid crisis continues to escalate. Naloxone can effectively reverse the effects of opioid overdose. We planned a randomized trial on the effectiveness of a point-of-care overdose education and naloxone distribution (OEND) intervention on participants’ performance in a simulated opioid overdose scenario. In preparation for the trial, we conducted a feasibility study which included a qualitative process evaluation aimed at eliciting participants’ perspectives of the study’s OEND tool and procedures, and how their lived experiences of the opioid crisis intersected with their experiences of the study. Twenty-three participants were interviewed, including people with lived experiences of opioid use or overdose, and people living in neighbourhoods or working in services where they were likely to encounter overdose. Thematic analysis of interview transcripts was informed by stigma theory. Participants’ accounts depicted challenges faced by people who take opioids in their everyday lives, deep losses experienced, negative attitudes encountered, and systemic barriers to care. Participation in the study itself was portrayed as meaningful. We explored participants’ experiences through three key themes: (1) who were the participants – describing their experiences related to opioid overdose, opioid use and attendant stigma; (2) why did they participate – recounting their motivations to join the study; and (3) what they thought about study processes – reflecting on the OEND materials and study procedures. Accounts revealed a sense of agency as participants confronted the opioid crisis. Our results demonstrate that people experiencing opioid use and overdose and people who care about them are eager and willing to be approached about research at point of care; participants were eager to learn overdose prevention skills and to return for follow-up study sessions. They recounted a range of motivations for participating, the most important of which is the opportunity to actively intervene, save lives and raise awareness. Trial Registration: ClinicalTrials.gov registry (NCT03821649)

Temperature Variation of the Local Structure and Dihydrogen Bonds in Ammonia Borane

fMRI insights into differential brain activation, executive function, and physical activity in older adults

Background Executive function is vital for cognitive health, particularly in older adults, where declines can lead to an increased risk of cognitive impairment. Physical activity (PA) has been linked to improvements in executive function, yet the underlying mechanisms remain poorly understood. Methods This cross-sectional study involved 41 Chinese adults (21 young: 23.0 ± 2.12 years; 20 older: 63.30 ± 2.36 years) who were categorized as physically active (≥3000 metabolic equivalent (MET)-min/week) or inactive (<3000 MET-min/week). Participants performed fMRI while completing executive function tasks (Flanker, N-back, Switching). Brain activation patterns were analyzed using Statistical Parametric Mapping (SPM), with significance thresholds set at p < 0.01 (voxel-level) and p < 0.05 (whole-brain corrected). Results Physically active older adults showed significantly better accuracy and faster reaction times on the Flanker task than inactive peers. In young adults, those who were inactive exhibited greater activation in prefrontal regions during executive tasks. No significant differences in brain activation were found in older adults for these tasks. Additionally, activation in the right medial/paracentral cingulate gyrus (BA 6) negatively correlated with working memory reaction times in active young adults (r= −0.804, p < 0.05), whereas cognitive flexibility in active older adults positively correlated with activation in the right dorsolateral frontal gyrus (BA 32; r = 0.589, p < 0.05). Conclusion Active older adults require less brain activation to perform executive function tasks, suggesting enhanced cognitive efficiency. In contrast, young adults showed different patterns of brain activation, indicating potential compensatory mechanisms. These results underscore PA’s role in optimizing age-specific cognitive strategies and underscore the need for longitudinal research to clarify causality.

Is AI watching you? The hidden links between research and surveillance

Design and comparative analysis of laser-based array systems for UAV detection in surveillance zones

Identifying unmanned aerial vehicles (UAVs) is critical to protecting vital locations and infrastructures from potential attacks. The literature suggests a variety of detection methods, including conventional radar systems, acoustic detection, radio frequency signal detection, LiDAR, and camera-based techniques. LiDAR systems, in particular, offer high-resolution 3D mapping and precise distance measurements, which prove to be highly effective for detecting and tracking UAVs under various environmental conditions. This study presents two innovative LiDAR systems for UAV detection: a multi-array static LiDAR system and a one-array rotating LiDAR system. The multi-array static LiDAR employs arrays of laser light sources and concentrators arranged along a spherical shape. A central photodiode receives the transmittance of the reflected optical energy captured by the concentrators, enabling the precise identification of UAVs. The system’s design focuses on achieving continuous, high-resolution coverage with minimal delay, making it suitable for monitoring wide areas. In contrast, the one-array rotating LiDAR utilizes a single array with rotational motion to scan the surveillance area. This approach prioritizes compactness and energy efficiency, which makes it advantageous for cost-sensitive applications. However, the rotational mechanism introduces trade-offs, such as increased mechanical wear and scanning latency. By conducting a comprehensive analysis of the design characteristics, this study evaluates the practicability and efficiency of these LiDAR solutions. Parameters such as the dimensions of the monitored region, sensor characteristics, component arrangement, and interspacing are considered to assess the effectiveness of both systems in identifying potential UAV threats.

Correction: The effects of tryptophan loading on Attention Deficit Hyperactivity Disorder in adults: A remote double blind randomised controlled trial

Screening of a lignin decomposing bacterium and its application in bamboo biomechanical pulping

Bamboo is an excellent raw material for papermaking, offering advantages such as a simple papermaking process, abundant availability, short growth cycle, and significant ecological effect. However, the lignin content in bamboo greatly restricts its effective utilization of bamboo pulp. In this study, a strain of SF-6, an efficient ligninolytic bacterium, was screened from bamboo rat feces under restrictive culture conditions and identified as Enterobacter sichuanensis by 16S rDNA. The main factors affecting the fermentation of SF-6 was determined by a one-way test, and the optimal culture temperature of the strain was 36.8 °C. The inoculum amount and pH value were determined by response surface analysis. The optimum culture temperature was 36.8 °C, the inoculum amount was 7.5%, and the initial pH was 5.2. Under these conditions, the decomposition rate of lignin was 38.59%, which was 55.42% higher than that before optimization, the paper tear resistance was improved by 73.2%, the breakage strength was improved by 62.41%, and the amount of alkali used was only 1.5 times that of the traditional chemical method. In conclusion, bamboo pulping with SF-6 resulted in a good pulping performance, less energy consumption, and no harm to the environment. Therefore, this is a feasible preparation method. This study provides a new microbial source for lignin degradation.

Statistical and machine learning models for predicting university dropout and scholarship impact

Although student dropout is an inevitable aspect of university enrollment, when analyzed, universities can gather information which enables them to take preventative actions that mitigate dropout risk. We study a data set consisting of 4,424 records from a Portuguese higher institution. In this study, dropout is defined from a micro-perspective, where field and institution changes are considered as dropouts independently of the timing these occur. The purpose of this analysis is twofold. First, we aim to build predictive models to learn of the significant socioeconomic and academic features associated with students’ dropout risk. Another goal is to understand the relationship between financial status and dropout, especially the causal effect of being a scholarship holder. Propensity score matching is conducted first with the training set to better estimate the causal effect of being a scholarship holder on dropout status while controlling confounding variables. The predictive classifiers evaluated are Lasso regression, generalized additive model, random forest, XGBoost and single-layer neural network. The XGBoost model has the highest F1-score 0.904. According to this model, the most important features predicting dropout status are the student’s second semester grades and the number of units they are credited. Whether a student’s tuition fee is up to date, whether they owe money to a debtor, whether they are scholarship holders, and students’ age at enrollment are also found to be important features. The Generalized Additive Model (GAM) performs competitively and offers clear interpretability, revealing how changes in actionable variables influence dropout risk. It shows that receiving a scholarship leads to the reduction in the odds of dropping out by nearly 40%, or by 22.2% in terms of probability when holding other factors fixed. As the study is based on data from a single institution and time period, and unobserved confounders cannot be fully ruled out, results should be interpreted with caution.

Association of long working hours with psychological distress in men with pregnant partners: An observational study from the Japan Environment and Children’s Study

Background It has been suggested that working long hours affects workers’ mental health, although findings have been inconsistent. In this study, we investigated the association of working hours with psychological distress in a population of Japanese men with pregnant partners, using data from the Japan Environment and Children’s Study. Methods Data from 44,996 men were analyzed and weekly working hours were classified into six groups. The Kessler Psychological Distress Scale (K6) was used to assess mental health. Each of the six items were assessed on a 5-point scale (0–4), with a total score of 0–24 and higher scores indicating greater psychological distress. A total score of 5–12 was considered to indicate moderate psychological distress and a score of ≥13 to indicate severe psychological distress. To investigate the association of working hours with psychological distress, multinomial logistic regression analysis was performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs). Results The results showed that after adjusting for covariates, weekly working hours was positively associated with moderate and severe psychological distress. Compared with men who worked ≤40 h per week, those who worked >55 to ≤65 h or >65 h per week had significantly higher ORs (95% CIs) for moderate psychological distress, 1.12 (1.03–1.21) and 1.34 (1.24–1.45), respectively, and those working >65 h per week had significantly higher OR, 1.84 (1.47–2.32) for severe psychological distress. For these two outcomes, a significant p for trend (<.0001) was observed in both the crude and adjusted models. Conclusion The results of this study suggest that the greater time constraints resulting from working long hours are associated with psychological distress in Japanese men with pregnant partners.

Impact of monitor unit optimization in volumetric modulated arc therapy planning for nasopharyngeal carcinoma

Purpose To evaluate the impact of monitor units (MUs) optimization on volumetric modulated arc therapy (VMAT) plan for nasopharyngeal carcinoma (NPC). Methods Twenty-one NPC patients were retrospectively analyzed. Dual-arc VMAT plan were designed using photon optimization algorithms without the monitor unit objective (MUO) tool, denoted as the base plan. Each base plan was re-optimized with the MUO tool with the Maximum MU parameter set to 30% of the base plans’ total MUs and Strength parameters set to 50, 80, and 100, generating plans S50, S80, and S100. Target and organ-at-risk (OAR) dose distributions, MUs, beam delivery time, and gamma passing rates were compared between re-optimized and base plans. Statistical analysis was performed using SPSS 17.0 (paired t-tests; significance: P < 0.05). Results Plan S100 reduced target PCTV2 D98% by >4% (relative to the base plan) in four patients. Plan S80 reduced target PGTV and PGTVnd Dmax and target PCTV2 D98% for >3% but <4% in two patients, while other target dose parameters changed by <2%. Compared to the base plan, all re-optimized plans increased the brainstem Dmax (P < 0.05), though the maximum increase was < 1.5%. Plan S50 reduced both parotid glands D50% and Dmean (P < 0.001), while plan S80 reduced both parotids Dmean and the left parotid D50% (P < 0.001). Conversely, S100 increased both parotids D50% and Dmean and the spinal cord Dmax (P < 0.05). Plan S80 and S100 increased the thyroid V40 (P < 0.05). MU reductions averaged 5.1% (S50), 21.4% (S80), and 30.9% (S100), with consistent beam delivery times (~2.5 minutes). Gamma passing rates improved sequentially from the base plan to S50, S80, and S100. Conclusion MU optimization in NPC VMAT planning effectively reduces MUs and enhances delivery accuracy (improved gamma passing rates). While target coverage and OAR sparing were generally maintained, higher MUO strengths (e.g., S100) may necessitate careful consideration of dosimetric trade-offs. Moderate MUO settings (e.g., S80) offer a favorable balance between MU reduction and plan fidelity.

Improved swin transformer-based thorax disease classification with optimal feature selection using chest X-ray

Thoracic diseases, including pneumonia, tuberculosis, lung cancer, and others, pose significant health risks and require timely and accurate diagnosis to ensure proper treatment. Thus, in this research, a model for thorax disease classification using Chest X-rays is proposed by considering deep learning model. The input is pre-processed by resizing, normalizing pixel values, and applying data augmentation to address the issue of imbalanced datasets and improve model generalization. Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. To further improve feature selection, we utilize the Chaotic Whale Optimization (ChWO) Algorithm, which optimally selects the most relevant attributes from the extracted features. Finally, the disease classification is performed using the novel Improved Swin Transformer (IMSTrans) model, which is designed to efficiently process high-dimensional medical image data and achieve superior classification performance. The proposed EnAE + ChWO+IMSTrans model for thorax disease classification was evaluated using extensive Chest X-ray datasets and the Lung Disease Dataset. The proposed method demonstrates enhanced Accuracy, Precision, Recall, F-Score, MCC and MAE of 0.964, 0.977, 0.9845, 0.964, 0.9647, and 0.184 respectively indicating the reliable and efficient solution for thorax disease classification.

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