Nevin Manimala

PLoS One. 2026 Jan 27;21(1):e0341187. doi: 10.1371/journal.pone.0341187. eCollection 2026.

ABSTRACT

Hospital readmissions prolong patient suffering and increase healthcare expenditures. Unplanned readmission rates, such as those reported by the Center for Medicare & Medicaid Services (CMS), distinguish between planned and unplanned readmissions. However, within unplanned readmissions, there is no distinction between those that are preventable versus unpreventable by the hospitals. Alternative approaches attempting to identify potentially preventable readmissions directly from coded medical data have been explored but have shown low sensitivity. Consequently, identifying preventable readmissions remains a time-consuming task for hospital quality managers seeking to allocate improvement resources effectively. To address this challenge, we aimed to develop and evaluate a probabilistic approach to improve the identification of preventable readmissions among unplanned readmissions. Using a retrospective record review of 600 single inpatient stays from a tertiary referral hospital group in Switzerland, we investigated the hypothesis that readmitted patients with a low expected probability for readmission (based on a logistic regression model using patient characteristics) would retrospectively show higher odds of having experienced a potentially or most likely preventable readmission (as assessed by the reviewers). The results confirmed our hypothesis: patients in the third with the lowest expected probability of readmission (compared with those in the highest third) had 6.6 and 8.7 times higher odds, respectively, of having experienced a potentially or most likely preventable readmission. Among preventable readmissions, the leading causes of readmission were surgical complications, medication-related reasons, nonsurgical complications, and premature discharge. Our proposed probabilistic approach can be used by hospital quality managers to focus case finding efforts on unexpectedly readmitted patients and aid effective resource allocation for improvement initiatives.

PMID:41592101 | DOI:10.1371/journal.pone.0341187

PLoS One. 2026 Jan 27;21(1):e0341870. doi: 10.1371/journal.pone.0341870. eCollection 2026.

ABSTRACT

INTRODUCTION: Maternal and child undernutrition remains a major public health challenge globally, with the highest burdens observed in low- and middle-income countries. Coexisting maternal and child undernutrition has serious implications for survival, growth, and quality of life.Maternal and child undernutrition is a complex, multifactorial issue influenced by a web of interconnected determinants including health, socio-economic status, education and environmental conditions.

OBJECTIVES: To examine the spatial distribution and multilevel determinants of coexisting maternal and child undernutrition in Ethiopia using EDHS data from 2000-2016.

METHODS: We analyzed a weighted sample of 33,445 participants from four consecutive EDHS surveys. Spatial autocorrelation, hotspot, and interpolation analyses were conducted using ArcMap 10.8. Multilevel logistic regression was performed in Stata 17. Cluster variability was assessed using ICC, MOR, and PCV. Variables with p < 0.05 were considered statistically significant.

RESULT: A total of 33,445 weighted sample was used for this study. The prevalence of coexisting maternal and child undernutrition was 22.87% (95% CI: 22.42, 23.32). Spatial analysis result showed that hot spot areas were concentrated in the northern regions especially Tigray, Amhara, and parts of Benishangul-Gumuz. Multilevel logistics regression analysis result revealed that maternal primary education (AOR = 0.88; 95% CI: 0.78, 0.98), secondary/higher education (AOR = 0.38; 95% CI: 0.29, 0.49), medium household wealth (AOR = 0.85; 95% CI: 0.75, 0.97), high household wealth (AOR = 0.78; 95% CI: 0.69, 0.89), child age 12-23 months (AOR = 2.77; 95% CI: 2.44, 3.16), ANC use (AOR = 0.83; 95% CI: 0.75, 0.92), improved toilet (AOR = 0.83; 95% CI: 0.71, 0.98) and regions.

CONCLUSION AND RECOMMENDATIONS: Coexisting maternal and child undernutrition shows marked geographic clustering in northern Ethiopia. Strengthening maternal education, improving household economic conditions, enhancing ANC use, and expanding sanitation services particularly in high-risk regions are essential to reduce the burden.

PMID:41592092 | DOI:10.1371/journal.pone.0341870

JCI Insight. 2026 Jan 27:e197261. doi: 10.1172/jci.insight.197261. Online ahead of print.

ABSTRACT

The survival of patients with acute myelogenous leukemia (AML) carrying mutations in TP53 is dismal. We report the results of a detailed characterization of responses to treatment ex vivo with the MDM2 inhibitor MI219, a p53 protein stabilizer, in AML blasts from 165 patients focusing analyses on TP53 wildtype (WT) patients. In total 33% of AML were absolute resistant to MDM2 inhibitor induced apoptosis, of which 45% carried TP53 mutation and 55% were TP53 WT. We conducted array-based expression profiling of ten resistant and ten sensitive AML cases with WT TP53 status, respectively, at baseline and after 2h and 6h of MDM2 inhibitor treatment. While sensitive cases showed the induction of classical TP53 response genes, this was absent or attenuated in resistant cases. In addition, the sensitive and resistant AML samples at baseline profoundly differed in the expression of inflammation-related and mitochondrial genes. No TP53 mutated AML patient survived. The 4-year survival of AML with defective MDM2 inhibitor induced TP53-mediated apoptosis despite WT TP53 was dismal at 19% when NPM1 was co-mutated and 6% when NPM1 was WT. In summary, we identified prevalent multi-causal defects in TP53-mediated apoptosis in AML resulting in extremely poor patient survival.

PMID:41592087 | DOI:10.1172/jci.insight.197261

Cereb Cortex. 2026 Jan 6;36(1):bhaf346. doi: 10.1093/cercor/bhaf346.

ABSTRACT

Frontal midline theta oscillations are key neural markers for learning, set-shifting, and adaptive behavior, signaling cognitive control and the reorganization of neural representations. The present study explored how these oscillations mediate the extraction and updating of statistical regularities. We delivered 6-Hz in-phase or sham transcranial alternating current stimulation, synchronizing frontal midline theta during an eye-tracking probabilistic sequence learning task designed to test cognitive flexibility and assess changes in pre-stimulus gaze direction. A novel probabilistic sequence with a partially overlapping structure was introduced that allowed us to distinguish between the retention of old sequences and the acquisition of new ones. Following comparable statistical learning in both groups during the stimulation session, our results showed that frontal midline theta synchronization enhances the adaptation of predictive processes shown by the reduction of erroneous anticipations of previously learned regularities and more flexible anticipation of novel regularities. These results suggest a role of frontal midline theta in the flexible rewiring of the mental representations of prior probabilistic structures and in making predictions more accurate.

PMID:41592083 | DOI:10.1093/cercor/bhaf346

PLoS One. 2026 Jan 27;21(1):e0341320. doi: 10.1371/journal.pone.0341320. eCollection 2026.

ABSTRACT

Breast cancer is a highly heterogeneous malignant tumor, and its accurate classification is of great significance for clinical diagnosis and treatment decision-making. In recent years, convolutional neural networks and Transformer have been widely used in pathological image analysis of breast cancer. Though the former excels at capturing local information and the latter is adept at modeling global dependencies, the former is limited by fixed sampling positions and is hindered to characterize irregular cell morphology, the latter is insufficient in describing the two-dimensional spatial structure of cells and tissues. Based on these, a Spatial-Frequency Domain Feature Extraction Model (S-FDFEM) proposed in this paper integrates spatial and frequency domain information to enhance feature learning for pathological image recognition. Specifically, in the spatial domain, Deformable Bottleneck Convolution (DBottConv) is utilized to effectively represent intricate cell and tissue morphological variations in pathological images and improve the expression ability of local features; in the frequency domain, wavelet low frequencies and Fourier high frequencies are generated based on the input pathological images to capture global approximation and fine structures, then statistical transformer and depth gradient feature extraction modules are integrated to operate on these two frequency domain components, enabling global dynamic focusing and two dimensions spatial characterization of pathological images. Experimental results show that the classification of breast cancer pathological image on BreakHis and BACH datasets verify the superiority of the S-FDFEM proposed in this paper.

PMID:41592082 | DOI:10.1371/journal.pone.0341320

PLoS One. 2026 Jan 27;21(1):e0341152. doi: 10.1371/journal.pone.0341152. eCollection 2026.

ABSTRACT

Fourier-transform near-Infrared (FT-NIR) technology offers a promising alternative to traditional methods for detecting soil Chromium (Cr) contamination. However, the relationship between soil Cr content and the spectra may involve complex non-linear dynamics and data redundancy. Therefore, selecting spectral feature variables and constructing parametric scaling models for rapid estimation has become a focal point in current research. In this study, the parametric scaling support vector machine (PSSVM) method is proposed for optimizing the modeling parameters, the binary modified differential evolution (BDE) algorithm is designed for selecting the feature variables. In combination, a novel combined optimization system is established by embedding the PSSVM model into the BDE iterative process. The system (BDE-PSSVM) is validated by estimating the soil Cr content based on the FT-NIR spectral data. The soil samples are collected from the area around a centralized waste treatment base, serving as the research subject. The original spectral data underwent preprocessing using Savitzky-Golay smoothing. Subsequently, the samples were divided into the training and testing sets by the SPXY algorithm, where the testing samples are strictly excluded from the model training process. Feature selection and the parametric scaling model optimization are simultaneously performed by applying the BDE-PSSVM model. The most optimal model observes the minimal root mean square error of 8.114, which only carries 56 discrete variables. In comparison to some other counterpart modeling methods, the BDE-PSSVM uses less feature variables and yields the better prediction results. This finding indicates that the proposed BDE-PSSVM modeling system provides an efficient way for rapid estimation of soil Cr content in cooperation with the FT-NIR technology. The proposed system is expected to undergo testing for its application in detecting additional analytes.

PMID:41592072 | DOI:10.1371/journal.pone.0341152

PLoS One. 2026 Jan 27;21(1):e0341570. doi: 10.1371/journal.pone.0341570. eCollection 2026.

ABSTRACT

BACKGROUND: The role of health insurance and its diverse hearing health benefits on hearing aid utilization is currently unknown. The objective of this study is to examine rates of ever and regular hearing aid (HA) use by insurance status in older U.S. adults.

METHODS: This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) (2005-2018). Older adults (≥65 years) with complete data on health insurance, audiometry, and hearing aid use (n = 3,172) were included. Eight combinatorial insurance categories were created and compared pairwise to the reference of Medicare only coverage. Outcomes included ever and regular hearing aid use.

RESULTS: Among older U.S. adults, 30.3% [95% CI:27.6%-33.2%] of those with audiometry-measured hearing loss reported ever using HAs while 22.9% [95% CI:20.3%-25.7%] reported regular HA use. Among older adults with hearing loss, those with military-related insurance (Tricare, VA and Champ-VA) had amongst the highest rates of ever and regular HA use (43.3% [95% CI:31.3%-56.2%] and 30.8% [95% CI:21.1%-42.5%], respectively). Ever HA use rates for individuals with Medicare and Medicaid was 30.8% [95% CI:27.8%-33.8%] and 17.7% [95% CI:11.9%-25.5%], respectively. In a multivariable model adjusting for demographics and hearing loss severity, individuals with military-related and military-related+Medicaid insurance were significantly more likely to report ever using HAs compared to those with Medicare only (OR 1.80, 95% CI:1.03-3.16; OR 20.38, 95% CI:1.07-386.84, respectively). Those with military-related insurance were more likely to report regular HA use (OR 2.17, 95% CI:1.16-4.09).

CONCLUSION: In this nationally representative study of older U.S. adults, we found differences in ever and regular HA use rates by insurance status, even when adjusting for hearing loss, demographics, and comorbidities. Future research is warranted to investigate group-specific differences, including access to hearing care, hearing health benefits, and stigma, to better understand the facilitators and barriers to hearing aid use by insurance status.

PMID:41592065 | DOI:10.1371/journal.pone.0341570

PLoS One. 2026 Jan 27;21(1):e0339721. doi: 10.1371/journal.pone.0339721. eCollection 2026.

ABSTRACT

Healthcare transition from pediatrics to adult care is a critical yet challenging process for adolescents with long-term medical conditions. This population-based cohort study aims to present a replicable method to identify and quantify adolescents in need of comprehensive transitional care. Using data from Danish national health registers, disease complexity was categorized by expert clinicians based on diagnoses indicative of a need for comprehensive transitional care and transfer to specialized adult healthcare. The study identified 4,677 adolescents requiring comprehensive transitional care from a background population of 418,994 Danish adolescents aged 16-17 years, corresponding to 1.1%. Analysis of outpatient visit data from tertiary hospitals revealed variability in the proportion of adolescents with comprehensive transitional care needs across Denmark’s four tertiary hospitals. For instance, 11.6% of outpatient visits at Aalborg University Hospital involved a comprehensive transition-requiring diagnosis, compared to 26.7% at Copenhagen University Hospital. While the method is intentionally specific and focused on adolescents with the most complex conditions, it offers a scalable framework that could be applied across broader clinical settings. We illustrate this by also applying the method within a pediatric department-based setting. This study provides al replicable framework to assess transition care needs at a population level, primarily identifying adolescents with the most complex conditions. Broader implementation across clinical settings may refine and inform equitable transitional strategies.

PMID:41592040 | DOI:10.1371/journal.pone.0339721

PLOS Digit Health. 2026 Jan 27;5(1):e0001190. doi: 10.1371/journal.pdig.0001190. eCollection 2026 Jan.

ABSTRACT

The global rise in prescription opioid use has contributed to an opioid epidemic, associated harms, and unintentional deaths in several western countries. Opioids however continue to be regularly prescribed for acute pain and in the chronic pain context due to limited treatment options. Currently there are no accurate tools that help predict which patients prescribed opioids may be at risk of death, which depends on the cultural context and varies across countries. Existing models do not account for statistical considerations such as censoring and competing risks. Using nationally representative data from the United Kingdom from 1,026,139 patients newly prescribed an opioid, we developed three competing risk time-to-event models: a regression model, a random forest, and a deep neural network to predict opioid-related deaths using UK primary care records. The models were externally validated in an external cohort of 337,015 patients. The models exhibited good discrimination and positive predictive value during internal validation (C-statistic for the regression model, random forest, and neural network: 84.3%, 84.4% and 82.1% respectively), and external validation (C-statistic for the regression model, random forest, and neural network: 81.8%, 81.5% and 81.5% respectively). Prior substance abuse, lung and liver comorbidities, morphine, fentanyl, or oxycodone at initiation and co-prescription of gabapentinoids were some of candidate predictors associated with a higher risk of opioid-related mortality within the models. These results demonstrate how routinely collected data from a nationally representative dataset may be used to develop and validate opioids risk algorithms to better help clinicians and patients predict risk to this serious adverse outcome.

PMID:41592035 | DOI:10.1371/journal.pdig.0001190

PLoS One. 2026 Jan 27;21(1):e0338434. doi: 10.1371/journal.pone.0338434. eCollection 2026.

ABSTRACT

BACKGROUND: Accurate sources on environmental nicotine exposure, such as biomarker data, remain insufficient in low- and middle-income countries. This study aimed to i) determine the optimal cut-off point of urinary cotinine that discriminates smokers from non-smokers, ii) estimate misclassification rate between self-reported smoking and urinary cotinine, and III) explore the distribution of tobacco smoke exposure levels using urinary cotinine concentrations among adults in Vietnam in 2024.

METHODS: A cross-sectional study was conducted in 2024 across seven provinces representing Vietnam’s ecological regions. Using multi-level stratified random sampling techniques, 1,077 adults aged 18-60 were recruited. Demographic and behavioural data were obtained through structured interviews. Urinary cotinine to creatinine ratios (CCR) were measured using high-performance liquid chromatography-tandem mass spectrometry. The Youden J method was used to determine the optimal cut-off point of CCR. Statistical analyses were performed using SPSS 20.0.

RESULTS: Self-reported results showed that 18.3% were active smokers, 33.4% were exposed to SHS at home, and 48.3% lived in a non-smoking household. The optimal CCR cut-off value of 20.947 µg/g can distinguish smokers and non-smokers with a sensitivity of 61.5%, specificity of 93.2%, 70.6% positive predictive value and 90% negative predictive value. Regional disparities and urinary cotinine among the non-smoking groups suggest potential environmental exposure to nicotine.

CONCLUSION: The CCR level of 20.947 µg/g indicated the optimal cut-off value to distinguish smokers and non-smokers. Vietnam was among countries with high levels of environmental nicotine exposure, with significant variation by sex, education, occupation, income, and region. Urinary cotinine is a reliable biomarker for nicotine exposure and should be integrated into routine surveillance. These findings support the need for stricter enforcement of smoke-free environments and interventions tailored to reduce involuntary tobacco exposure.

PMID:41592032 | DOI:10.1371/journal.pone.0338434