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Correction: A peripheral subepithelial network for chemotactile processing in the predatory sea slug Pleurobranchaea californica
Circulating lncRNA HOTAIR is a biomarker for pediatric acute lymphoblastic leukemia and mediator of miR-326 exosomal export
Removal of sulfate pollutant from different samples of a river water using nanozeolite technology, case study: Gamasiab River, Iran
Human activities significantly impact on river water quality as a crucial water source. A study in the Gamasiab River analyzed samples from 16 points at three time periods, assessing element concentrations. The most polluted station was identified using spectrophotometric testing and treated with natural and modified zeolite nanoparticles for purification. Various acid and base combinations modified the nanoparticles, optimizing their effectiveness as adsorbents through tests under different conditions. Utilizing the Design Expert model, theoretical adsorption values were determined based on pH and adsorbent-pollutant ratio. The modified samples demonstrated 77% efficiency with 0.2 molar nitric and sulfuric acid. Interaction studies showed how phosphate and nitrate ions affected sulfate adsorption. Optimal adsorption conditions were defined at pH = 9.6 and D/C = 17.01, achieving 86.5% pollutant adsorption. The Freundlich isotherm, with a coefficient of determination of 0.92, was chosen over the Langmuir isotherm (0.79) for its superior performance. Therefore, applying zeolite nanoparticles efficiently eliminated sulfate pollutants from surface water resources at the laboratory.
Berberine attenuates obesity-induced skeletal muscle atrophy via regulation of FUNDC1 in skeletal muscle of mice
Impact of parental education on number of under five children death per mother in Bangladesh
One of the leading challenges of social development is the reduction of children’s deaths under the age of five. The primary focus of this research is to study the potential impact of parental education on under five children death in Bangladesh utilizing a secondary dataset extracted from the Bangladesh Demographic and Health Survey (BDHS), 2017–18. The total count of deceased children within a family is a non-negative numerical variable. The mean number of under five children death per 100 mothers is found to be 20 with variance of around 27, which indicates the presence of overdispersion. As the response variable exhibits 84.2% zero counts, we have considered three regression models in this research; Poisson model, zero-inflated Poisson model, and zero-inflated negative binomial model. Finally, zero-inflated negative binomial model, exhibiting the lowest AIC value, indicates that both maternal and paternal education have significant protective impact on under five children death. Specifically, greater levels of formal education achieved by the parents are associated with a decreased rate of children death.
A Compact 2-D photonic crystal biomedical sensor for enhanced glucose concentration detection in urine
Abstract This study introduces a 2-D Photonic Crystal (PhC) biosensor designed, simulated, and evaluated for detecting glucose concentrations in urine by utilizing refractive index variations. The sensor demonstrates exceptional performance, achieving a sensitivity of 20,040.30 nm/RIU for glucose levels ranging from 0–15 mg/dl, a quality factor of 10,424.55, and a detection limit as low as 8 × 10−10, surpassing benchmarks reported in the literature. With compact dimensions of 16.8 × 17.6 µm2 and compatibility with modern fabrication techniques, the proposed design is well suited for integration into portable diagnostic devices. A comprehensive comparative analysis underscores its superior sensitivity, ultra-high quality factor, and compact design, establishing it as a major advancement in glucose detection technology.
Climate change effects on ecosystem services: Disentangling drivers of mixed responses
Climate change is a pervasive hazard that impacts the supply and demand of ecosystem goods and services (EGS) that maintain human well-being. A recent review found that the impacts of climate change on EGS are sometimes mixed, posing challenges for managers who need to adapt to these changes. We expand on earlier work by exploring drivers of varying responses of EGS to climate within studies. We conducted a systematic review of English-language papers directly assessing climate change impacts on the supply, demand, or monetary value of ‘provisioning EGS’, ‘regulating EGS’, or ‘cultural EGS’. Ultimately, 44 papers published from December 2014 to March 2018 were analyzed. Nearly 66% of EGS were assessed for higher-income countries despite how lower-income countries disproportionately face negative climate impacts. Around 59% of observations or projections were mixed responses of EGS to climate change. Differences in climate impacts to EGS across space or climate scenarios were the most common causes of mixed responses, followed by mixed responses across time periods assessed. Disaggregating findings by drivers is valuable because mixed responses were often due to multiple drivers of variation. Carefully considering the decision context and desired outcome of a study will help select appropriate methodology to detect EGS variation. Although studies have often assessed relevant drivers of variation, assessing interactions of other sources of uncertainty and both climate and non-climate drivers may support more effective management decisions that holistically account for different values in the face of uncertainty.
IVC treatment between primary and second TURBT may improve the prognosis of high-risk NMIBC patients receiving BCG treatment
Automated gait event detection for exoskeleton-assisted walking using a long short-term memory model with ground reaction force and heel marker data
Traditional gait event detection methods for heel strike and toe-off utilize thresholding with ground reaction force (GRF) or kinematic data, while recent methods tend to use neural networks. However, when subjects’ walking behaviors are significantly altered by an assistive walking device, these detection methods tend to fail. Therefore, this paper introduces a new long short-term memory (LSTM)-based model for detecting gait events in subjects walking with a pair of custom ankle exoskeletons. This new model was developed by multiplying the weighted output of two LSTM models, one with GRF data as the input and one with heel marker height as input. The gait events were found using peak detection on the final model output. Compared to other machine learning algorithms, which use roughly 8:1 training-to-testing data ratio, this new model required only a 1:79 training-to-testing data ratio. The algorithm successfully detected over 98% of events within 16ms of manually identified events, which is greater than the 65% to 98% detection rate of previous LSTM algorithms. The high robustness and low training requirements of the model makes it an excellent tool for automated gait event detection for both exoskeleton-assisted and unassisted walking of healthy human subjects.
Contrast-enhanced magnetic resonance imaging based calf muscle perfusion and machine learning in peripheral artery disease
Classifying COVID-19 hospitalizations in epidemiology cohort studies: The C4R study
Rationale Robust COVID-19 outcomes classification is important for ongoing epidemiology research on acute and post-acute COVID-19 conditions. Protocolized medical record review is an established method to validate endpoints for clinical trials and cardiovascular epidemiology cohorts; however, a protocol to adjudicate hospitalizations for COVID-19 among epidemiology cohorts was lacking. Objectives We developed a protocol to ascertain and adjudicate hospitalized COVID-19 across a meta-cohort of 14 US prospective cohort studies. This report describes the first three years of protocol implementation (October 1, 2020—October 1, 2023) and evaluates its repeatability and performance compared to classification by administrative codes. Methods The protocol was adapted from cohort approaches to clinical cardiovascular events ascertainment and adjudication. Potential COVID-19 hospitalizations and deaths were identified by self-/proxy-report and, in some cases, active surveillance. Medical records were requested from hospitals and adjudicated for COVID-19 outcomes by clinically trained personnel according to a standardized rubric. Inter-rater agreement was assessed. The sensitivity and specificity of discharge diagnosis codes was compared to adjudicated diagnoses. Measurements and main results The study obtained medical records for 1,167 potential COVID-19 hospitalizations, which underwent protocolized adjudication. Adjudication confirmed COVID-19 infection was present for 1,030 (88%) events, of which COVID-19 was not the cause of hospitalization for 78 (8%). Of 952 hospitalizations determined by adjudicators to be caused by COVID-19, 319 (34%) participants were critically ill and 210 (22%) died. Pneumonia was confirmed in 822 (86%) and acute kidney injury in 350 (37%); other cardiovascular and thrombotic complications were rare (2–5%). Interrater reliability among adjudicators was high (kappa = 0.85–1.00) except for myocardial infarction (kappa = 0.60). Compared to adjudication, sensitivity of discharge diagnosis codes was higher for pneumonia (84%) and pulmonary embolism (81%) than for other complications (48–70%). Conclusions Protocolized adjudication confirmed four out of five COVID-19 hospitalizations in a US meta-cohort and confirmed cases of pneumonia, pulmonary embolism, and other conditions that were not indicated by discharge diagnosis codes. These results highlight the importance of validating health outcomes for use in research on COVID-19 and post-COVID-19 conditions, and some limitations of claims-based data.
Spin-dependent thermoelectric properties of a hybrid ferromagnetic metal/quantum dot/topological insulator junction
Abstract The thermoelectric properties of hybrid system based on a single-level quantum dot coupled to a ferromagnetic metallic lead and attached to the surface states of a three-dimensional topological insulator are theoretically investigated. On the surface of a three-dimensional topological insulator, massless helical Dirac fermions emerge. We calculate the thermoelectric coefficients, including electrical conductance, Seebeck coefficient (thermopower), heat conductance, and the figure of merit, using the nonequilibrium Green’s function technique. The results are analyzed in terms of the emergence of new effects. The calculations are performed within the Hubbard I approximation concerning the dot’s Coulomb interactions. Additionally, the spin-dependent coupling of the quantum dot to the ferromagnetic lead lifts the spin degeneracy of the dot’s level, which influences the transport properties of the system. We incorporate this effect perturbatively to obtain the spin-dependent renormalization of the dot’s level. We also consider the case of finite spin accumulation in the ferromagnetic electrode, which leads to spin thermoelectric effects.
MAP1LC3C repression reduces CIITA- and HLA class II expression in non-small cell lung cancer
In the last decade, advancements in understanding the genetic landscape of lung squamous cell carcinoma (LUSC) have significantly impacted therapy development. Immune checkpoint inhibitors (ICI) have shown great promise, improving overall and progression-free survival in approximately 25% of the patients. However, challenges remain, such as the lack of predictive biomarkers, difficulties in patient stratification, and identifying mechanisms that cancers use to become immune-resistant (“immune-cold”). Analysis of TCGA datasets reveals reduced MAP1LC3C expression in cancer. Further analysis indicates that low MAP1LC3C is associated with reduced CIITA and HLA expression and with decreased immune cell infiltration. In tumor cells, silencing MAP1LC3C inhibits CIITA expression and suppresses HLA class II production. These findings suggest that cancer cells are selected for low MAP1LC3C expression to evade efficient immune responses.
Association between rheumatoid arthritis and interstitial lung disease and impact of serologic status: a large-scale longitudinal study
Assessment of public awareness and perspectives towards adverse drug reaction reporting system in Karachi, Pakistan
Background Public involvement in reporting adverse drug reactions (ADRs) generates a broader database on drug safety. Underreporting remains a hindrance to implementing an effective pharmacovigilance system that ultimately affects public health. Hence, it is critical to appraise the public’s awareness of ADR reporting and pharmacovigilance to address the gaps for the enhancement of ADR reporting rate. Objectives The current study explored public knowledge and attitudes toward ADR reporting in Karachi, Pakistan. Methods A quantitative cross-sectional study was conducted from 3rd Jan 2022 to 30th Nov 2022 using a forty-item questionnaire to evaluate public insights regarding the ADR and its reporting. Descriptive analysis was executed to determine frequencies and percentages for the respondents’ baseline characteristics and the responses toward ADR reporting. The chi-square test (χ2) was applied to determine the association between the dependent and independent variables considering a p-value < 0.05 as statistically significant. Results The response rate of the present study was 78.3%. More than 80% of the respondents deemed that ADR occurs only with high doses of medicines and over-the-counter medications do not cause any ADR. More than 75% of the respondents did not know that the ADR reporting form is available on the Drug Regulatory Authority of Pakistan (DRAP) website; the response varied significantly with the education (p = 0.002) and social status (p = 0.0001) of the respondents. More than 50% of the participants refused to ever report an ADR to health professionals. Physicians (n = 364; 47.7%) and pharmacists (n = 253; 33.1%) were the respondents’ professed most reliable sources to whom ADR can be reported; responses varied significantly with their education (p = 0.003) and age (p = 0.001). Conclusions The study has provided insight into the challenges and gaps needed to improve ADR reporting in Pakistan. The outcomes revealed that the public is aware of the benefits of reporting ADRs; however, they do not realize their role and the potentially significant impact on the healthcare system by contributing to ADR reporting. Therefore, it is a need of time to educate the public on the value of reporting ADRs and implement user-friendly and accessible ADR reporting systems in patient care areas to facilitate easier reporting.
Author Correction: Memory reactivation generates new, adaptive behaviours that reach beyond direct experience
Role of the platelet-lymphocyte ratio as a prognostic indicator in patients with intracranial hemorrhage: A systematic review and meta-analysis
Background The prognostic value of platelet-lymphocyte ratio (PLR) in ischemic stroke had been investigated in previous studies. However, the results of studies on PLR in patients with intracranial hemorrhage (ICH) are inconsistent. We aimed to conduct a meta-analysis to determine the prognostic value of PLR in predicting functional outcome and mortality in patients with ICH. Methods We searched the databases of PubMed, Embase, the Cochrane Library, and CNKI for relevant studies up to 10th June 2024. The Newcastle Ottawa Quality Assessment Scale (NOS) was applied to evaluate the quality of the included studies. We calculated the pooled odds ratios (OR) with 95% confidence intervals (CI) between PLR and both functional outcome (as measured by the modified Rankin Scale, mRS) as well as mortality. Poor functional outcomes were defined as mRS > 2. Results A total of 6 studies with 2992 patients were included. The random effects meta-analysis demonstrated that elevated PLR exhibited an association with poor functional outcome in patients with ICH (OR = 1.69; 95% CI [1.39–2.07]; P<0.0001; I2 = 24%). Similarly, elevated PLR was associated with mortality in patients with ICH (OR = 1.65; 95% CI [1.12–2.43]; P = 0.01; I2 = 31%). Conclusion This study suggested that elevated PLR was significantly associated with poor functional outcome (mRS>2) and increased mortality, indicating that elevated PLR could serve as a reliable a prognostic factor for unfavorable clinical outcomes in patients with ICH. It is advisable to conduct extensive prospective investigations across diverse ethnic backgrounds to verify the accuracy of this correlation prior to its utilization in clinical settings.
A prosperous and thorough analysis of gravity profiles for resources exploration utilizing the metaheuristic Bat Algorithm
Identifying children who develop severe chronic kidney disease using primary care records
Background Understanding whether symptoms suggestive of chronic kidney disease (CKD) are reported to primary care before diagnosis may provide opportunities for earlier detection, thus supporting strategies to prevent progression and improve long-term outcomes. Our aim was to determine whether symptoms/signs or consultation frequency recorded in primary care could predict a subsequent diagnosis of chronic kidney disease in children. Methods We undertook a case-control study within Clinical Practice Research Datalink. Cases were children <21 years with an incident code for severe CKD during the study period (January 2000-September 2018). Controls were matched on age (+/-3 years), sex, and practice-level kidney function testing rate. Conditional logistic regression modelling was used to identify symptoms predictive of severe CKD and differences in consultation frequency in 24- and 6-month timeframes before the index date. Results Symptoms predictive of severe CKD in the 24 months before the index date included growth concerns (OR 7.4, 95% CI 3.5, 15.4), oedema (OR 5.7, 95% CI 2.9, 11.2) and urinary tract infection (OR 3.3, 95% CI 2.1, 5.4); within 6 months of the index date, effect estimates and specificity strengthened although sensitivity decreased. Overall, positive predictive value of symptoms was low. Cases consulted more frequently than controls in both timeframes. In combination, symptoms and consultation frequency demonstrated modest discrimination for CKD (c-statistic after bootstrapping 0.70, 95% CI 0.66, 0.73). Conclusion Despite increased consultation frequency and several symptoms being associated with severe chronic kidney disease, the positive predictive value of symptoms is low given disease rarity making earlier diagnosis challenging.