Tag: nevin manimala

Chin Med. 2025 Jun 6;20(1):80. doi: 10.1186/s13020-025-01127-9.

ABSTRACT

This study addresses limitations of mainstream approaches in traditional Chinese medicine (TCM) data mining by developing the SinoMedminer R package and its Shiny web application. The R package’s core functionalities include data cleaning, transformation, TCM attribute statistics, association rule exploration and analysis, clustering analysis, co-occurrence network analysis, formula similarity analysis, formula identification, and dosage analysis. This package enables efficient project analyses without requiring complex coding. The accompanying Shiny web application provides an interactive, menu-driven interface for users without programming knowledge. SinoMedminer combines the computational power of a programming language with user-friendly accessibility, significantly enhancing the efficiency and standardization of TCM data mining research. A deployed server platform further simplifies access and usability by allowing direct utilization of the Shiny application. By optimizing data processing and analysis workflows, SinoMedminer enhances big data handling capabilities, accelerates research progress and product development, and promotes the integration of digital technologies into TCM research and clinical practice.

PMID:40481553 | DOI:10.1186/s13020-025-01127-9

Inj Epidemiol. 2025 Jun 6;12(1):31. doi: 10.1186/s40621-025-00583-z.

ABSTRACT

BACKGROUND: The injury rate is a common measure of injury occurrence in epidemiological surveillance and is used to express the incidence of injuries as a function of both the population at risk as well as at-risk exposure time. Traditional approaches to surveillance-based injury rates use a frequentist perspective; here, we discuss the Bayesian perspective and present a practical framework on how to apply a Bayesian analysis to estimate injury rates. We estimated finescale injury rates across a broad range of categories for men’s and women’s soccer, applying a Bayesian methodology and using injury surveillance data captured within the National Collegiate Athletic Association Injury Surveillance Program from 2014/15-2018/19.

RESULTS: Through an iterative process of assessing model fidelity, we found that a negative binomial model was an effective choice for modeling surveillance-based injury rates. We also found differences between schools to be a key driver of variation in injury rates.

CONCLUSIONS: Our findings indicate that the Bayesian framework naturally characterizes injury rates by modeling injury counts as outcomes of an underlying data-generation process that explicitly incorporates inherent uncertainty, complementing traditional frequentist approaches. Key benefits of the Bayesian approach in this context are the ability to test model suitability in a variety of methods, and to be able to generate plausible estimates with sparse data.

PMID:40481549 | DOI:10.1186/s40621-025-00583-z

BMC Nutr. 2025 Jun 6;11(1):113. doi: 10.1186/s40795-025-01090-6.

ABSTRACT

BACKGROUND: Coconut oil has been suggested as a potential dietary intervention for weight management. However, the evidence regarding the effects of coconut oil supplementation on anthropometric measures (body weight, body mass index (BMI) and waist circumference (WC)) remains inconclusive.

OBJECTIVE: we aimed to assess the overall effect of coconut oil supplementation on these anthropometric parameters and explore potential sources of heterogeneity.

METHODS: We comprehensively searched electronic databases using appropriate keywords. We included 15 studies with the following criteria: (1) clinical trials in adults, with parallel or cross-over design, (2) evaluated the effect of coconut oil on body weight, BMI or WC, (3) compared the effect of a specific dose of coconut oil against a coconut oil-free diet or other types of oils, (4) considered the change in anthropometric parameters as the primary or one of the secondary outcomes, (5) provided mean and standard deviation (SD) of change in anthropometric parameters across study arms, (6) reported the number of participants in each study arm.

RESULTS: The trials included 620 participants and assessed the effects of coconut oil supplementation on body weight, BMI and WC. Our meta-analysis revealed statistically significant effects of coconut oil supplementation on weight and BMI, with mean differences of 0.04 kg (95% CI: 0.01 to 0.08 kg) and 0.01 kg/m2 (95% CI: 0.00 to 0.02). However, the effects were not clinically meaningful. There was no significant effect of coconut oil on WC. Subgroup analyses suggested that the duration of the intervention may influence the effect of coconut oil on body weight. In the sensitivity analysis, we found that the result of one study influenced the associations between coconut oil supplementation and weight or BMI.

CONCLUSIONS: Overall, our findings suggest no clinically significant effects of coconut oil supplementation on weight loss. Further research is needed to clarify the issue.

SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD420251031291.

PMID:40481535 | DOI:10.1186/s40795-025-01090-6

Orphanet J Rare Dis. 2025 Jun 6;20(1):284. doi: 10.1186/s13023-025-03729-w.

ABSTRACT

BACKGROUND: Neurofibromatosis type 1 is a genetic disease with an autosomal dominant pattern. One of its clinical features is the presence of disfiguring neurofibromas. Most adults with Neurofibromatosis type 1 have visible neurofibromas depending on the severity of their skin related clinic that can affect their body image, and body image influencing psychological assistance and social support. This research explored Body image, the negative perception of the appearance of neurofibromas and skin severity in Neurofibromatosis type 1 patients; assessed its association with quality of life; and the role of social support and psychological assistance.

RESULTS: Two hundred five patients with Neurofibromatosis type 1 (16-74 years) were included in the study. They responded to questionnaires about their quality of life, body image and other sociodemographic data. Correlations and simple and multiple regressions were used to assess the relationships between variables. The results showed that body image problems increased if Neurofibromatosis type 1 patients were concerned about the aspects of their neurofibromas (B = 4.544; p < 0.001) and if they had severe skin conditions (B = 4.262; p < .001). Despite this, statistical analysis showed that only body image impairments reduced quality of life by 0.605 (p < 0.001), while skin severity and the negative perception of the appearance of neurofibromas were not clearly related. Patients with body image impairments are more likely to seek psychological assistance (ρ = 0.218; p < 0.01), but they are less likely to report having social support. The results also showed that when patients with Neurofibromatosis type 1 retrieved they have social support (ρ = -0.210, p < 0.01) or they inform doing psychological assistance (ρ = -0.238; p < 0.001), they have lower quality of life.

CONCLUSION: Body image concerns, rather than skin severity, are a key feature for detecting quality of life impairments in these patients. When healthcare professionals detect body image impairments, it is crucial for them to collaborate with patients and either provide or refer them to psychological interventions. This approach helps improve social support, enabling patients to benefit from both their professional and personal environments.

PMID:40481533 | DOI:10.1186/s13023-025-03729-w

Crit Care. 2025 Jun 6;29(1):230. doi: 10.1186/s13054-025-05468-7.

ABSTRACT

BACKGROUND: Large language models (LLMs) have demonstrated potential in assisting clinical decision-making. However, studies evaluating LLMs’ diagnostic performance on complex critical illness cases are lacking. We aimed to assess the diagnostic accuracy and response quality of an artificial intelligence (AI) model, and evaluate its potential benefits in assisting critical care residents with differential diagnosis of complex cases.

METHODS: This prospective comparative study collected challenging critical illness cases from the literature. Critical care residents from tertiary teaching hospitals were recruited and randomly assigned to non-AI-assisted physician and AI-assisted physician groups. We selected a reasoning model, DeepSeek-R1, for our study. We evaluated the model’s response quality using Likert scales, and we compared the diagnostic accuracy and efficiency between groups.

RESULTS: A total of 48 cases were included. Thirty-two critical care residents were recruited, with 16 residents assigned to each group. Each resident handled an average of 3 cases. DeepSeek-R1’s responses received median Likert grades of 4.0 (IQR 4.0-5.0; 95% CI 4.0-4.5) for completeness, 5.0 (IQR 4.0-5.0; 95% CI 4.5-5.0) for clarity, and 5.0 (IQR 4.0-5.0; 95% CI 4.0-5.0) for usefulness. The AI model’s top diagnosis accuracy was 60% (29/48; 95% CI 0.456-0.729), with a median differential diagnosis quality score of 5.0 (IQR 4.0-5.0; 95% CI 4.5-5.0). Top diagnosis accuracy was 27% (13/48; 95% CI 0.146-0.396) in the non-AI-assisted physician group versus 58% (28/48; 95% CI 0.438-0.729) in the AI-assisted physician group. Median differential quality scores were 3.0 (IQR 0-5.0; 95% CI 2.0-4.0) without and 5.0 (IQR 3.0-5.0; 95% CI 3.0-5.0) with AI assistance. The AI model showed higher diagnostic accuracy than residents, and AI assistance significantly improved residents’ accuracy. The residents’ diagnostic time significantly decreased with AI assistance (median, 972 s; IQR 570-1320; 95% CI 675-1200) versus without (median, 1920 s; IQR 1320-2640; 95% CI 1710-2370).

CONCLUSIONS: For diagnostically difficult critical illness cases, DeepSeek-R1 generates high-quality information, achieves reasonable diagnostic accuracy, and significantly improves residents’ diagnostic accuracy and efficiency. Reasoning models are suggested to be promising diagnostic adjuncts in intensive care units.

PMID:40481529 | DOI:10.1186/s13054-025-05468-7

Acta Epileptol. 2025 Jun 6;7(1):35. doi: 10.1186/s42494-025-00224-4.

ABSTRACT

The effectiveness of treatment for the chronic phase of febrile infection-related epilepsy syndrome (FIRES) remains uncertain. This study aimed to evaluate the therapeutic efficacy of minocycline in patients with chronic FIRES who had a poor response to conventional antiseizure medications. Three patients received 100 mg of minocycline (100 mg twice daily for 12 weeks), with effectiveness assessed based on seizure frequency, duration, type, and quality of life (using the quality of life in epilepsy-31, QOLIE-31), alongside adverse event monitoring. Results showed that one patient (Patient 3) exhibited a significant reduction in seizure duration and improved QOLIE-31 scores, with focal seizures being the only type observed after treatment. However, there was no statistically significant change in overall seizure frequency among the three patients. Additionally, a short literature review was conducted to explore various management strategies for chronic FIRES, including IL-1 receptor antagonist (anakinra) and IL-6 receptor antagonist (tocilizumab), centro-median thalamic nuclei deep brain stimulation, cannabidiol, responsive neurostimulation, intrathecal dexamethasone, ketogenic diet, and vagus nerve stimulation. In conclusion, considering the existing research on the etiological mechanisms of FIRES and based on our preliminary findings on the anti-inflammatory and antiepileptic properties of minocycline, early initiation of minocycline therapy in the chronic phase of FIRES should be explored further.Trial registrationClinicaltrials.gov (NCT05958069, retrospectively registered 22 July 2023).

PMID:40481521 | DOI:10.1186/s42494-025-00224-4

Crit Care. 2025 Jun 6;29(1):232. doi: 10.1186/s13054-025-05420-9.

ABSTRACT

BACKGROUND: Despite widespread vaccination efforts, effective treatment strategies remain critical for severe SARS-CoV-2 infection. Tumour necrosis factor-alpha (TNF-α) plays a central role in the cytokine storm characteristic of severe COVID-19. This systematic review and meta-analysis evaluates the effectiveness, efficacy, and safety of TNF-α inhibitors in the management of COVID-19.

PATIENTS AND METHODS: A systematic review of PubMed, Embase, and CENTRAL was conducted, focusing on studies involving SARS-CoV-2-infected patients treated with TNF-α inhibitors compared with those receiving standard of care without prior TNF-α inhibitor use. Data from studies published up to August 12, 2024, were analysed. Outcomes assessed included mortality, invasive mechanical ventilation, and C-reactive protein (CRP) levels. Odds ratios (ORs) and mean differences (MD) were calculated with 95% confidence intervals (CI), and subgroup analyses were performed for randomised controlled trials (RCTs) and non-randomised studies.

RESULTS: Seven studies involving 1393 patients with moderate-to-critical COVID-19 were included. TNF-α inhibitor treatment was associated with a reduced odds of mortality (OR 0.67, 95% CI [0.44-1.00], P = 0.052), which was statistically significant in the RCT subgroup across three studies (OR 0.75, 95% CI [0.58-0.97], P = 0.042, certainty of evidence: very low). The number needed to treat for mortality was calculated to be 16 (95% CI 9.0-inf.), which indicates that one additional death could be avoided for every 16 patients treated with TNF-α inhibitors compared to standard of care. No significant reduction in the need for invasive mechanical ventilation was observed (OR 0.95 [95% CI 0.46-1.94]; P = 0.822). Additionally, TNF-α inhibitors resulted in a significant reduction in CRP levels (MD – 21.9 mg/L [95% CI – 38.46 to – 5.34]; P = 0.024) within three to seven days post-treatment.

CONCLUSION: Our study indicates a potential role for TNF-α inhibition in the treatment of COVID-19 as their use was associated with reduced mortality, but further studies are needed to provide robust evidence.

PMID:40481519 | DOI:10.1186/s13054-025-05420-9

BMC Public Health. 2025 Jun 6;25(1):2124. doi: 10.1186/s12889-025-23215-z.

ABSTRACT

BACKGROUND: Respiratory infections claim lives, especially the lives of children under the age of five, around the world. In South Sudan, respiratory infection has been identified as one of the three leading causes of death among children. In addition to other sources of indoor air pollution, cooking elsewhere inside the house but not in the kitchen is a major contributor to indoor air pollution. South Sudan has no access to clean energy for cooking and relies entirely on biomass. Therefore, this study aims to examine the effect of indoor air pollution, proxied by cooking location, on the occurrence of respiratory infection among children under five in South Sudan.

METHOD: The study used the 2010 South Sudan Household Health Survey 2 data, with 6,307 observations of under-five children. To address potential endogeneity, a Two-Stage Residual Inclusion was employed within the logistic regression framework. Additionally, a control function approach was adopted to account for unobserved heterogeneity, if any, in the model.

RESULTS: Cooking elsewhere inside houses but not in the kitchens increases the probability of respiratory infection among children under the age of five in South Sudan. Control variables such as the roofing of the house, the gender of the under-five child, and the gender of the head of the household also influence the probability of respiratory infection among this age group.

CONCLUSION: Cooking elsewhere inside houses but not in the kitchens while using biomass for cooking has been dangerous for the health of children under the age of five. To save lives, especially those of children, cooking should be done either in a kitchen or outside of houses in the interim while moving towards clean energy for cooking.

PMID:40481513 | DOI:10.1186/s12889-025-23215-z

J Phys Chem A. 2025 Jun 6. doi: 10.1021/acs.jpca.5c01146. Online ahead of print.

ABSTRACT

DFTB+ is a flexible, open-source software package developed by its community, designed for fast and efficient atomistic quantum mechanical simulations. It employs various methods that approximate density functional theory (DFT), such as density functional-based tight binding (DFTB) and the extended tight binding (xTB) approach allowing simulations of large systems over extended time scales with reasonable accuracy, while being significantly faster than traditional ab initio methods. In recent years, several new extensions of the DFTB method have been developed and implemented in the DFTB+ program package in order to improve the accuracy and generality of the available simulation results. In this paper, we review those enhancements, show several use case examples and discuss the strengths and limitations of its features.

PMID:40479742 | DOI:10.1021/acs.jpca.5c01146

Nucleic Acids Res. 2025 Jun 6;53(11):gkaf469. doi: 10.1093/nar/gkaf469.

ABSTRACT

We describe a DNA-array-based method to infer intramolecular connections in a population of RNA molecules in vitro. First we add DNA oligonucleotide “patches” that perturb the RNA connections, and then we use a microarray containing a complete set of DNA oligonucleotide “probes” to record where perturbations occur. The pattern of perturbations reveals couplings between regions of the RNA sequence, from which we infer connections as well as their prevalences in the population, without reference to folding models. We validate this patch-probe method using the 1058-nucleotide RNA genome of satellite tobacco mosaic virus (STMV), which has been shown to have multiple long-range connections. Our results not only indicate long-range connections that agree with previous structures but also reveal the prevalence of competing connections. Together, these results suggest that multiple structures with different connectivity coexist in solution. Furthermore, we show that the prevalence of certain connections changes when pseudouridine, an important component of natural and synthetic RNAs, is substituted for uridine in STMV RNA, and that the connectivity of STMV minus strands is qualitatively distinct from that of plus strands. Finally, we use a simplified version of the method to validate a predicted 317-nucleotide connection within the 3569-nucleotide RNA genome of bacteriophage MS2.

PMID:40479708 | DOI:10.1093/nar/gkaf469