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Cost-effectiveness of broadly neutralizing antibodies for HIV prophylaxis for infants born in settings with high HIV burdens
Background Approximately 130 000 infants acquire HIV annually despite global maternal antiretroviral therapy scale-up. We evaluated the potential clinical impact and cost-effectiveness of offering long-acting, anti-HIV broadly neutralizing antibody (bNAb) prophylaxis to infants in three distinct settings. Methods We simulated infants in Côte d’Ivoire, South Africa, and Zimbabwe using the Cost-Effectiveness of Preventing AIDS Complications-Pediatric (CEPAC-P) model. We modeled strategies offering a three-bNAb combination in addition to WHO-recommended standard-of-care oral prophylaxis to infants: a) with known, WHO-defined high-risk HIV exposure at birth (HR-HIVE); b) with known HIV exposure at birth (HIVE); or c) with or without known HIV exposure (ALL). Modeled infants received 1-dose, 2-doses, or Extended (every 3 months through 18 months) bNAb dosing. Base case model inputs included 70% bNAb efficacy (sensitivity analysis range: 10–100%), 3-month efficacy duration/dosing interval (1–6 months), and $20/dose cost ($5–$100/dose). Outcomes included pediatric HIV infections, life expectancy, lifetime HIV-related costs, and incremental cost-effectiveness ratios (ICERs, in US$/year-of-life-saved [YLS], assuming a ≤ 50% GDP per capita cost-effectiveness threshold). Findings The base case model projects that bNAb strategies targeting HIVE and ALL infants would prevent 7–26% and 10–42% additional pediatric HIV infections, respectively, compared to standard-of-care alone, ranging by dosing approach. HIVE-Extended would be cost-effective (cost-saving compared to standard-of-care) in Côte d’Ivoire and Zimbabwe; ALL-Extended would be cost-effective in South Africa (ICER: $882/YLS). BNAb strategies targeting HR-HIVE infants would result in greater lifetime costs and smaller life expectancy gains than HIVE-Extended. Throughout most bNAb efficacies and costs evaluated in sensitivity analyses, targeting HIVE infants would be cost-effective in Côte d’Ivoire and Zimbabwe, and targeting ALL infants would be cost-effective in South Africa. Interpretation Adding long-acting bNAbs to current standard-of-care prophylaxis would be cost-effective, assuming plausible efficacies and costs. The cost-effective target population would vary by setting, largely driven by maternal antenatal HIV prevalence and postpartum incidence.
A qualitative study to examine hidden care burden for older adults with overweight and obesity in England
Objectives The study aimed to explore the phenomena related to formal and informal social care needs among overweight and obese older adults living in England. Background Despite the rising prevalence of obesity among older adults, its impact on social care needs remains underexplored. Existing research highlights significant unmet social care needs among older adults, yet the specific challenges faced by those who are overweight or obese have received limited attention. This study addresses this gap by exploring and understanding the social care experiences and needs of older adults in England who are overweight or obese. Methods Participants were recruited from a local National Health Service (NHS) health centre in London England using a purposive sampling strategy to the point of analytical saturation. A total of 45 participants were invited and of these 33 participants were eligible to take part. All participants in this study are either of British origin or immigrants to the UK from various nationalities. A semi-structured interview was conducted, and a qualitative structural narrative analysis was undertaken. Results The study found that older adults, who are overweight or obese, were more likely to have physical health problems and problems with mobility. They were more likely to have informal voluntary care and support rather than formal social care support. They also had a weaker social support network, were more isolated and frustrated, lacked housing adaptations, felt unsafe, felt they were a burden to their families and felt discriminated against by the wider community. Care and support needs if not met, then these are likely to generate or widen health inequalities over time. Conclusions This study provides a unique perspective on unmet care needs among overweight and obese older adults in England. It highlights the compounded challenges faced by this population, emphasising the importance of holistic social care approaches that address both health and psychosocial needs. Findings suggest that minimal yet targeted interventions, such as accessible support networks and public health policies promoting social engagement, could significantly improve wellbeing and reduce long-term health inequalities.
A systematic review of the best-practice return to play programs in tennis players
Tennis is one of the most practiced sports in the world and with high injury rates across professional players and effective strategies to return to sport after an injury are necessary. Thus, this systematic review aims to assess and identify the most effective and evidence-based protocols developed during the return to play process in tennis players. The search was conducted using Web of Science, PubMed, and Scopus electronic databases. Studies that report a structure training program after suffering an injury in tennis players published before October 12, 2023, were identified. A total of 1164 studies were identified, after removal of duplicates and assess full text for eligibility 5 studies were included in the systematic review. RTP (return-to-play process) should be divided in three phases. Firstly, training should be focused on restoring ROM through mobility exercises, the inclusion of technical training are also available. Following, prioritize reaching pre-injury strength levels, integrating more technical training increasing hitting velocity. Finally, include serve technique while gradually increasing velocity. Trainers and coaching staff professionals’ needs consider that the duration and progression of RTP should be tailored to the individual characteristics of each player.
Role of hematological parameters in the early detection of clinical cases for septicemia among neonates: A hospital-based study from Chennai, India
Background Neonatal sepsis, a leading cause of newborn mortality, arises from systemic infections due to an immature immune system. Its subtle early symptoms complicate timely diagnosis. Hematological parameters act as an indicator for early detection, crucial for prompt treatment, improving prognosis, and are not a challenging or cumbersome process. Aim The primary objective was to evaluate the significance of hematological parameters including red blood cell (RBC), WBC, and platelet counts in the context of neonatal sepsis. Methods This hospital-based cohort study examined 73 neonates admitted to the neonatal intensive care unit (NICU) of Saveetha Medical College and Hospital, Chennai, India during the period of January 2023 to March 2024. All the new born patients were presented with blood culture-confirmed septicemia. Results The investigation identified Klebsiella pneumoniae as the most prevalent etiological agent (26.02%), followed by Coagulase-Negative Staphylococci (CONS) and Acinetobacter baumannii (both 8.2%). Alterations in total leukocyte count and hematocrit were observed in 57% and 68.1% of cases, respectively, providing a prompt indication of infection status. Subsequent analyses revealed prominent leukocytosis, hematocrit irregularities, and thrombocytopenia, frequently manifesting in septic cases and demonstrating potential as early markers for neonatal sepsis. Conclusion The study highlights the diagnostic value of hematological alterations, such as leukocytosis and hematocrit distortion, in the prompt identification of septicemia among neonates. Based on the findings, it is recommended that routine hematological screening to be integrated as a standard component of neonatal sepsis diagnosis for rapid investigation of neonatal sepsis.
A new marker for predicting sentinel lymph node metastasis in early (cT1-2N0) breast cancer: Tumor-infiltrating lymphocytes (TILs)
Background Tumor-infiltrating lymphocytes (TILs) are associated with lymph node metastasis and prognosis in breast cancer. Therefore, we explored the value of TILs in predicting sentinel lymph node metastasis (SLNM) in patients with early-stage (cT1-2N0) breast cancer and provided a new method for preoperative assessment of SLNM status. Methods This study included 337 patients with early-stage breast cancer who underwent surgery at our hospital from January 2022 to December 2023. The expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67 in the patients was assessed using immunohistochemistry (IHC). TILs in the core needle biopsy samples were evaluated histopathologically, and patients were divided into high and low TILs groups based on the density of TILs. Statistical analysis was conducted, and a predictive model was established. Results The study found that patients with high TILs had a significantly lower rate of SLNM compared to those with low TILs (P < 0.001). The cT stage and the level of TILs were identified as independent predictive factors for SLNM. The ROC curve analysis indicated that the density of TILs has good predictive efficacy for SLNM. Based on the results of the multivariate regression analysis, a nomogram predictive model for SLNM was constructed. Conclusions Our study showed that the density of TILs and cT stage are independent predictive factors for SLNM in early-stage (cT1-2N0) breast cancer, and the predictive effect of TILs density on SLNM is significant in Luminal and triple-negative breast cancers.
A new stage for predicting the prognosis of granulomatous lobular mastitis
Introduction The prognosis of granulomatous lobular mastitis (GLM) had been poor, primarily due to the lack of a unified disease assessment standard. Objective The purpose of this cohort study was to establish a staging system for GLM to more accurately evaluate the prognosis of patients. Methods This study retrospectively collected data from 264 GLM patients who visited our hospital between January 2017 and December 2023. Through logistic regression analysis, factors associated with prognosis were identified, which served as the basis for creating a new staging system. Results Univariate and multivariate logistic regression analysis revealed that hyperlipidemia (HR: 2.031; 95% CI: 1.100-3.750) and microabscesses (HR: 2.087; 95% CI = 1.138-3.827) were significant independent risk factors affecting the prognosis of GLM patients. Based on the results of logistic analysis, three different stages were ultimately established, and it was found that stage C had the highest AUC value (AUC: 0.642), followed by stage B (AUC: 0.628), with stage A (AUC: 0.614) having the lowest. The Delong test revealed no significant difference in AUC values between stage A and stage B (P = 0.255), nor between stage B and stage C (P = 0.263). However, the AUC value of the stage C was found to be higher than that of stage A (P < 0.001). Given that stage C has the highest AUC value, this study selected stage C as the final stage for evaluating the prognosis of GLM patients and named it the 1st edition of GLM stage. Conclusions This study constructed a rigorous and widely applicable GLM staging system (the 1st edition of GLM stage). The system demonstrated good predictive outcomes and provided strong support for clinical decision-making.
Role of tristability in the robustness of the differentiation mechanism
During cell differentiation, identical pluripotent cells undergo a specification process marked by changes in the expression of key genes, regulated by transcription factors that can inhibit the transcription of a competing gene or activate their own transcription. This specification is orchestrated by gene regulatory networks (GRNs), encompassing transcription factors, biochemical reactions, and signalling cascades. Mathematical models for these GRNs have been proposed in various contexts, to replicate observed robustness in differentiation properties. This includes reproducible proportions of differentiated cells with respect to parametric or stochastic noise and the avoidance of transitions between differentiated states. Understanding the GRN components controlling these features is crucial. Our study thoroughly explored an extended version of the Toggle Switch model with auto-activation loops. This model represents cells evolving from common progenitors in one out of two fates (A or B, bistable regime) or, additionally, remaining in their progenitor state (C, tristable regime). Such a differentiation into populations with three distinct cell fates is observed during blastocyst formation in mammals, where inner cell mass cells can remain in that state or differentiate into epiblast cells or primitive endoderm. Systematic analysis revealed that the existence of a stable non-differentiated state significantly impacts the GRN’s robustness against parametric variations and stochastic noise. This state reduces the sensitivity of cell populations to parameters controlling key gene expression asymmetry and prevents cells from making transitions after acquiring a new identity. Stochastic noise enhances robustness by decreasing sensitivity to initial expression levels and helping the system escape from the non-differentiated state to differentiated cell fates, making the differentiation more efficient.
Remedial colon hydrotherapy device enema as a salvage strategy for inadequate bowel preparation for colonoscopy: A retrospective cohort study
Objective Colon hydrotherapy devices serve as a physiotherapeutic modality to manage colonic disorders by promoting intestinal peristalsis and enhancing gastrointestinal functionality. This study aims to assess and compare the effectiveness, safety, and tolerability of two remedial strategies for inadequate bowel preparation: colon hydrotherapy device enema and oral polyethylene glycol electrolyte powder. Methods A retrospective analysis was performed on 109 patients who failed to adequately prepare for colonoscopy. These patients received remedial bowel preparation on the same day as their procedure, with 55 undergoing colon hydrotherapy enema and 54 receiving oral polyethylene glycol electrolyte powder. Patient satisfaction and tolerance were evaluated through telephone follow-up. Key metrics included the Boston Bowel Preparation Scale scores, preparation time, incidence of adverse reactions, and patient tolerance and satisfaction. Results No significant differences were observed in baseline characteristics between groups (P > 0.05). The Boston Bowel Preparation Scale scores for the entire colon were 7 (3) in the hydrotherapy group and 6.5 (1) in the oral group (z = -2.075, P = 0.038). Notably, scores for the left colon were significantly higher in the hydrotherapy group [3 (1) vs. 2 (0), z = -5.586, P < 0.001]. The hydrotherapy group also exhibited a shorter preparation time [80 (20) min vs. 92.5 (20) min, z = -3.961, P < 0.001] and a lower incidence of adverse effects (36.4% vs. 88.9%, χ² = 32.035, P < 0.001). Patient satisfaction metrics, including re-selection rates and tolerance of side effects, were significantly higher in the hydrotherapy group. Conclusions The colon hydrotherapy device enema is an effective, efficient, and well-tolerated method for bowel cleansing, demonstrating a low incidence of adverse events. It is recommended as an effective and safe remedial therapy for patients with inadequate bowel preparation prior to colonoscopy.
Ensemble-based multiclass lung cancer classification using hybrid CNN-SVD feature extraction and selection method
Lung cancer (LC) is a leading cause of cancer-related fatalities worldwide, underscoring the urgency of early detection for improved patient outcomes. The main objective of this research is to harness the noble strategies of artificial intelligence for identifying and classifying lung cancers more precisely from CT scan images at the early stage. This study introduces a novel lung cancer detection method, which was mainly focused on Convolutional Neural Networks (CNN) and was later customized for binary and multiclass classification utilizing a publicly available dataset of chest CT scan images of lung cancer. The main contribution of this research lies in its use of a hybrid CNN-SVD (Singular Value Decomposition) method and the use of a robust voting ensemble approach, which results in superior accuracy and effectiveness for mitigating potential errors. By employing contrast-limited adaptive histogram equalization (CLAHE), contrast-enhanced images were generated with minimal noise and prominent distinctive features. Subsequently, a CNN-SVD-Ensemble model was implemented to extract important features and reduce dimensionality. The extracted features were then processed by a set of ML algorithms along with a voting ensemble approach. Additionally, Gradient-weighted Class Activation Mapping (Grad-CAM) was integrated as an explainable AI (XAI) technique for enhancing model transparency by highlighting key influencing regions in the CT scans, which improved interpretability and ensured reliable and trustworthy results for clinical applications. This research offered state-of-the-art results, which achieved remarkable performance metrics with an accuracy, AUC, precision, recall, F1 score, Cohen’s Kappa and Matthews Correlation Coefficient (MCC) of 99.49%, 99.73%, 100%, 99%, 99%, 99.15% and 99.16%, respectively, addressing the prior research gaps and setting a new benchmark in the field. Furthermore, in binary class classification, all the performance indicators attained a perfect score of 100%. The robustness of the suggested approach offered more reliable and impactful insights in the medical field, thus improving existing knowledge and setting the stage for future innovations.
Protocol for socioecological study of autism, suicide risk, and mental health care: Integrating machine learning and community consultation for suicide prevention
Introduction Autistic people experience higher risk of suicidal ideation (SI) and suicide attempts (SA) compared to non-autistic people, yet there is limited understanding of complex, multilevel factors that drive this disparity. Further, determinants of mental health service receipt among this population are unknown. This study will identify socioecological factors associated with increased risk of SI and SA for autistic people and evaluate determinants of mental health care receipt. Methods This study will link information for individuals aged 12-64 years in healthcare claims data (IBM® MarketScan® Research Database and CMS Medicaid) to publicly available databases containing community and policy factors, thereby creating a unique, multilevel dataset that includes health, demographic, community, and policy information. Machine learning data reduction methods will be applied to reduce the dimensionality prior to nested, multilevel empirical estimation. These techniques will allow for robust identification of clusters of socioecological factors associated with 1) risk of SI and SA and 2) receipt of mental health services (type, dose, delivery modality). Throughout, the research team will partner with an established group of autistic partners to promote community relevance, as well as receive input and guidance from a council of policy and practice advisors. Discussion We hypothesize that nested individual (co-occurring conditions, age, sex), community (healthcare availability, social vulnerabilities), and policy factors (state mental health legislation, state Medicaid expansion) will be associated with heightened risk of SI and SA, and that receipt, dose, and delivery of mental health services will be associated with interdependent factors at all three levels. The approach will lead to identification of multilevel clusters of risk and factors that facilitate or impede mental health service delivery. The study team will then engage the community partners, and policy and practice advisors to inform development of recommendations to reduce risk and improve mental health for the autistic population.
Neurologic manifestations of COVID-19 and viral test in cerebrospinal fluid
Background Neurological manifestations are present in about one-third of COVID-19 cases, ranging from mild symptoms, such as anosmia, to more severe forms like demyelinating syndromes. Although direct invasion of the CNS has been demonstrated, the immune- mediated pathway is also described and more accepted. Even in cases where viral detection in CSF is absent, it should not rule out neuroinvasion. There are few prospective studies about neurological manifestations of COVID-19, especially with viral tests in CSF; as well there are still many questions about COVID-19 associated with neurological disease. Thus, we describe clinical and CSF findings of a prospective cohort of patients with nasal positive tests for SARS-CoV-2 and neurological involvement. We also discuss the pathogenic mechanisms related to these manifestations. Methods and Findings This is a prospective cohort study; 27 patients were evaluated according to clinical presentation, the time interval between COVID-19 diagnosis and onset of neurological alterations, syndromic diagnosis, imaging and CSF findings. Real time polymerase chain reaction for SARS-CoV-2 genome was performed in all CSF samples. 2 RT-PCR in spinal cord fluid resulted positive in 9 (33.3%) cases, five of them had a positive swab nasal test concomitant to neurologic disease. Respiratory signs were described in 12 out 27 patients, five of them with viral detection in CSF. White cell counts in CSF were normal range in the majority of cases, except for 3 occurrences: two patients had elevated CSF WBC counts and viral detection in CSF (10 and 36 cells/mm3) and one also had elevated CSF WBC count but viral detection in CSF was negative (21cells/mm3). The observed neurological signs encompassed a diverse neurologic spectrum, including seizures, paresis, gait abnormalities, headaches, alteration in consciousness and memory or cognitive impairment. Both imaging and CSF alterations exhibited non-specific characteristics. Syndromic diagnoses included stroke, dementia or cognitive impairments, Guillain-Barré Syndrome, encephalitis, encephalomyelitis, acute flaccid palsy and optical neuritis. Conclusions The patients in the present study had COVID-19 and neurologic involvement including a wide range of clinical manifestations. SARS-CoV-2 was detected in one-third of CSF samples, regardless of time interval between COVID-19 diagnosis and the onset of neurological signs. These conditions encompass various pathogenic pathways and the neuroinvasion potential of SARS-CoV-2 should be more studied.
ResNet18 facial feature extraction algorithm improved based on hybrid domain attention mechanism
In the research of face recognition technology, the traditional methods usually show poor recognition accuracy and insufficient generalization ability when faced with complex scenes such as lighting changes, posture changes and skin color diversity. To solve these problems, based on the improvement of adaptive boosting to improve the accuracy of face detection, the study proposes a residual network 18-layer face feature extraction algorithm based on hybrid domain attention mechanism algorithm. The study introduces channel-domain and spatial-domain attention mechanism to enhance the extraction of face image features. The outcomes indicated that the recognition accuracy of the proposed method on multiple face image datasets, labeled field face datasets, and celebrity facial attribute datasets exceeded 98.34% and reached up to 99.64%, which was better than the current state-of-the-art methods. After combining channel and spatial attention mechanism, the false detection rate was as low as 2.50%, which was lower than the false detection rate of other methods. In addition to enhancing face recognition’s robustness and accuracy, the work offers fresh concepts and resources for face recognition’s potential uses in intricate scenarios in the future.
Comparing Academic Performance of Elementary Education Majors in General Education Science Courses
It is important for elementary teachers to understand the content they are responsible for teaching their students, known as content knowledge. In the content area of science, elementary teacher preparation programs often expect preservice teachers to develop content knowledge in college science courses completed prior to entering the program. These college science courses are often general education courses, not specifically designed for preservice elementary teachers. General education courses may not be adequately serving preservice elementary teachers. The purpose of this study is to explore the impact of general education science courses on preservice elementary teachers, as compared to other students at the same institution. We collected student grades in six different general education courses across ten years of instruction, resulting in a data set with 195860 grades. These data were analyzed using linear mixed modeling to predict course grades in each of the individual courses. Overall, these findings indicate that elementary education majors in general education courses are receiving grades similar to students in most other majors. Notably, elementary education majors received grades comparable to STEM majors in Biology, while scoring worse than STEM majors in Physical Science. These findings assuage some concerns about the impact of general education courses on elementary education majors and suggest that elementary education programs seeking to provide a specialized science course may want to prioritize a course in physical science.
Online risk exposure and anxiety among college students in China: The chain mediating role of negative attribution and interpersonal security
Despite evidence supporting the relationship between online risk exposure and anxiety, the underlying mechanisms remain underexplored. This study aimed to construct and validate a chain-mediation structural equation model to investigate how online risk exposure influences anxiety among college students. Data was collected online in April 2023 from a sample of college students (N = 986, 64.6% female) using validated scales measuring online risk exposure, negative attribution, interpersonal security, and anxiety. The results showed that online risk exposure not only directly exacerbates anxiety but also indirectly affects it through three distinct pathways: negative attribution, interpersonal insecurity, and their chain relationship. These mediation effects accounted for 41.16%, 20.47%, and 4.65% of the total effects, respectively. These findings highlight the importance of targeted interventions aimed at reducing online risk exposure, improving negative attribution styles, and enhancing interpersonal security among college students. The study concludes by discussing its limitations and proposing directions for future research.
Three hospitalized non-critical COVID-19 subphenotypes and change in intubation or death over time: A latent class analysis with external and longitudinal validation
Background There are two subphenotypes of COVID-19 acute respiratory distress syndrome with differential responses to corticosteroids, but whether similar subphenotypes of hospitalized non-critical COVID-19 patients exist remains unknown. Objective To identify and validate non-critical COVID-19 subphenotypes at hospital admission that may elucidate pathobiology and facilitate heterogeneity-of-treatment effect analyses of clinical trials with non-critical COVID-19 patients. Methods We conducted a multi-center retrospective cohort study of adults hospitalized with COVID-19 who were not intubated or did not die within 24 hours of admission. We derived and externally and longitudinally validated subphenotypes during the wild-type and delta severe-acute-respiratory-syndrome-coronavirus-2 (SARS-CoV2) waves via latent class analysis using clinical and laboratory data at hospital admission. We trained XGBoost machine learning models to predict subphenotype. Results We analyzed data for 4,827 hospitalized non-critical COVID-19 patients: 2,077 wild-type wave Columbia University Medical Center (CUMC) and affiliate hospital derivation cohort patients; 1,214 wild-type wave Cornell Medical Center and affiliate hospital external validation cohort patients; and 1,536 delta wave CUMC and affiliate hospital longitudinal validation cohort patients. A three-class latent class model best fit each cohort identifying subphenotypes that were low-inflammatory, intermediate-inflammatory, and high-inflammatory with fibrinolysis, with increasing 90-day risk of intubation or death across subphenotypes in the wild-type wave. However, in the delta wave, the intermediate-inflammatory subphenotype had the lowest 90-day risk of intubation or death. XGBoost model area under the receiver-operating-curve was 0.96 in the testing dataset, and biomarkers of inflammation and cardiorenal dysfunction were the strongest predictors of subphenotype. Conclusion We identified three hospitalized non-critical COVID-19 subphenotypes that persisted through the wild-type and delta SARS-CoV2 waves. The intermediate-inflammatory subphenotype had the greatest relative improvement in intubation and survival over time with the standardized use of corticosteroids and other interventions. Our machine learning model can facilitate heterogeneity-of-treatment effect analyses of clinical trials of adults hospitalized with non-critical COVID-19.