Tag: nevin manimala

Cytokine. 2025 Nov 30;197:157076. doi: 10.1016/j.cyto.2025.157076. Online ahead of print.

ABSTRACT

Dynamic molecular changes in early life follow a robust ontogeny as the infant immune system adapts to the demands of its new environment. Studies of plasma immunomodulatory cytokines and chemokines have previously demonstrated ontogenetic patterns of immune development across the first week of life. However, how plasma cytokine and chemokines concentrations evolve over the first 4 months of life remains unknown. In this study, we examined plasma cytokine and chemokine concentrations in a longitudinal cohort of infants in Papua New Guinea (Oceania; n = 87) across the first four months of life. Using a multiplex assay, concentration of 41 cytokines and chemokines in peripheral blood plasma samples collected at Day of Life (DOL) 0 (i.e., birth), -7, -30, and – 128 were measured. Several cytokines and chemokines that shape cellular immunity demonstrated a statistically significant increase in concentration over the first four months of life, including CXCL10 (5.5-fold), IFNγ (8.8-fold), and IL-2 (1.7-fold). In contrast, other cytokines and chemokines significantly diminished with age, including CCL2 (0.12-fold), CXCL8 (0.35-fold), IL-6 (0.38-fold), and TGFα (0.43-fold). Plasma cytokine and chemokine concentrations appeared to be minimally affected by demographic factors such as birth season, gestational age, sex, or maternal age. The patterns and directionality of these observations largely mirrored those reported in previous cohorts, suggesting universal patterns of plasma cytokine and chemokine trajectories in early life. Overall, understanding early life trajectories of plasma cytokines and chemokines provides insight into human immune development and supports future studies analyzing cytokine trajectories in relation to health and disease.

PMID:41325679 | DOI:10.1016/j.cyto.2025.157076

Nurse Educ Today. 2025 Nov 13;158:106915. doi: 10.1016/j.nedt.2025.106915. Online ahead of print.

ABSTRACT

OBJECTIVE: To explore effects of credit bank on clinical communication skills, critical thinking skills, self-directed learning ability, professional identity and public stigma associated with hepatitis B patients of nursing interns in the infectious diseases department.

METHODS: Employing a table of random numbers method, 140 nursing interns from the infectious diseases department of a grade-A tertiary hospital in Hunan Province were selected at random between an intervention group and a control group. The control group received routine clinical teaching in the infectious diseases department, while the intervention group implemented the credit bank practice plan in addition to the routine teaching practice. After 4 weeks of training, the disparities in clinical communication skills, critical thinking skills, professional identity, self-directed learning ability, and reduction of public stigma towards hepatitis B patients were examined between the two groups of nursing interns.

RESULTS: Statistics revealed that after 4 weeks of intervention, the scores of clinical communication skills, critical thinking skills, professional identity, and self-directed learning ability of the intervention group were significantly higher in the intervention group than in the control group, while the scores of public stigma against hepatitis B patients were significantly lower in the intervention group compared to the control group, and the differences were statistically significant (P < 0.05).

CONCLUSION: The results of this study show that credit bank teaching practice can reduce the public stigma of hepatitis B patients among nursing interns in the infectious diseases department and improve their clinical communication skills, critical thinking skills, professional identity and self-directed learning ability.

PMID:41325672 | DOI:10.1016/j.nedt.2025.106915

Clin Neurol Neurosurg. 2025 Nov 28;261:109277. doi: 10.1016/j.clineuro.2025.109277. Online ahead of print.

ABSTRACT

PURPOSE: The use of neuronavigation with superimposed mapping tools has enabled visualization of key fiber tracts and improved peri-operative planning. However, a limitation of these approaches is their reliance on a static underlying brain atlas, particularly in neurosurgical patients with brain tumors. A tool that enables qualification and quantification of brain region connectivity could refine approaches to surgical resection.

METHODS: We utilized a machine learning imaging platform, Quicktome™, to generate individualized functional parcels and tracts that dynamically adapt to perioperative change. The connectome was derived from a combination of diffusion tensor imaging and resting-state function magnetic resonance imaging. Matrices were generated from the functional MRI of four patients with intracranial neoplasms and the pre- and post-operative parcellation values were compared. The individual correlation and strength of regions were quantified. Hypo- and hyper-connected regions were marked as anomalous.

RESULTS: We present a case series of four patients to illustrate the correlation of the anomaly matrices with post-operative neurological changes. These include: post-operative delirium originating associated with salience network hypoconnectivity; visual hemineglect linked to hypoconnectivity in the dorsal attention network; and quantifiable improvements in the language network following the resolution of expressive aphasia. All differences between pre-and post-operative paired correlation values were statistically significant.

CONCLUSION: We demonstrate a novel approach to quantifying the extent to which anomalies in the functional connectome correlate with post-operative neurological changes. This has relevance in post-operative prognostication, provision of specialist therapy services, and could serve as a useful tool in surgical education and pre-operative planning.

PMID:41325661 | DOI:10.1016/j.clineuro.2025.109277

Nanotechnology. 2025 Dec 1. doi: 10.1088/1361-6528/ae2626. Online ahead of print.

ABSTRACT

Memristors with multilevel storage capabilities have emerged as promising candidates for high-density memory and neuromorphic computing systems. In this study, a trilayer-structured memristor with an Al2O3/HfO2/Al2O3 (3/14/3 nm) dielectric stack was fabricated via atomic layer deposition (ALD), sandwiched between Ti and Pt electrodes. The analog switching characteristics of the memristor were systematically investigated through two strategies: adjusting the compliant current (Icc) during the SET process and controlling the RESET-stop voltage (VRESET-stop) in the RESET process. The experimental results indicate that Icc primarily modulates the values of low resistance states (LRSs), whereas VRESET-stop mainly influences the values of high resistance states (HRSs). To validate multilevel storage feasibility, Icc values of 0.5, 1, 2.5, and 5 mA and VRESET-stop voltages of 1.5, 1.7, 2, and 2.3 V were systematically applied. Statistical analysis demonstrated that VRESET-stop modulation yields more stable and repeatable resistance states compared to Icc tuning. Furthermore, the continuous resistance (or conductance) tuning capability of our fabricated memristor emulates neural network weight updates. This allows trained weights to be directly mapped to the memristor’s conductance states, achieving 91.6% accuracy in handwritten digit recognition. This work underscores the significant potential of the Al2O3/HfO2/Al2O3 trilayer-structured memristor for high-performance multilevel storage and neuromorphic computing applications.

PMID:41325628 | DOI:10.1088/1361-6528/ae2626

JMIR Public Health Surveill. 2025 Dec 1;11:e83450. doi: 10.2196/83450.

ABSTRACT

BACKGROUND: Men who have sex with men (MSM) face a disproportionately high risk of mpox infection, and China has recently experienced a rapid increase in the reported cases. This population also has a high prevalence of HIV, which has been identified as a critical factor in understanding the vulnerability to mpox. In addition, metabolic diseases frequently co-occur with HIV and share immunometabolic pathways, raising concerns that they may interact to confer additional risk of mpox infection.

OBJECTIVE: This study examines the potential interaction between HIV and metabolic comorbidity in relation to self-reported mpox among MSM in China.

METHODS: A cross-sectional study was conducted among MSM aged 18 to 76 years from October 2023 to March 2024 in 6 representative provincial regions of China. Participants completed an anonymous questionnaire on HIV infection, metabolic diseases (hypertension, diabetes mellitus, and hyperlipidemia), and mpox infection. Metabolic comorbidity was defined as the presence of more than one of these conditions. Logistic regression models were used to examine associations, and additive and multiplicative interactions between HIV and metabolic comorbidity were assessed.

RESULTS: Of the 2403 MSM, 56 (2.33%) reported mpox, 199 (8.28%) reported HIV, and 325 (13.52%) reported at least one metabolic comorbidity (hypertension, diabetes, or hyperlipidemia). Both HIV (odds ratio [OR] 4.81, 95% CI 2.29-9.64) and metabolic comorbidity (OR 2.62, 95% CI 1.27-5.14) were associated with higher odds of mpox infection. A dose-response relationship was observed, with the odds of mpox increasing with the number of conditions (per-condition trend: OR 3.03, 95% CI 1.86-4.83). While multiplicative interaction was not statistically significant (interaction term=2.98, 95% CI 0.68-13.70; P=.15), additive interaction metrics suggested a possible excess association (relative excess risk due to interaction=10.80, 95% CI 1.21-37.52; attributable proportion due to interaction=0.74, 95% CI 0.07-0.87; synergy index=4.99, 95% CI 1.19-20.86). Compared to the participants without HIV or metabolic comorbidity, those with HIV and metabolic comorbidity had higher odds of mpox infection (OR 14.51, 95% CI 4.83-40.70).

CONCLUSIONS: This study suggests that HIV and metabolic comorbidity were each associated with higher odds of self-reported mpox, and exploratory analyses indicated a possible additive interaction. Given the reliance on self-reported diagnoses and the cross-sectional design, the findings should be interpreted with caution due to reporting bias and reverse causation. Further studies are needed to confirm these associations and better understand the comprehensive health needs of MSM with co-occurring conditions.

PMID:41325604 | DOI:10.2196/83450

JMIR Form Res. 2025 Dec 1;9:e81098. doi: 10.2196/81098.

ABSTRACT

BACKGROUND: In Argentina, diabetes is a growing public health concern, with a prevalence of 14% in 2024 and projections reaching 15.4% by 2050. In this context, a Diabetes Prevention and Care Program was implemented in low-income areas across 3 provinces. A key component of the program was a WhatsApp (WhatsApp LLC)-based intervention aimed at promoting self-care, encouraging healthy behaviors, and supporting follow-up among people with diabetes, those at risk, and pregnant women.

OBJECTIVE: This study aimed to describe the implementation and process evaluation of a WhatsApp-based intervention within Argentina’s public health system, using the Carroll Implementation Fidelity Framework, focusing on challenges encountered, implementation strategies used, and lessons learned across the 3 target populations.

METHODS: The intervention was implemented in 40 primary care centers. The population included adults residing in the catchment areas of the selected primary care centers. Participants included adults with type 2 diabetes, people at moderate or high risk based on the Finnish Diabetes Risk Score, and pregnant women. A set of 192 educational and reminder messages was developed and validated through expert input and community feedback. Messages were tailored to each target population and delivered through WhatsApp via Twilio (Twilio Inc) Business API (application programming interface). We assessed implementation fidelity focusing on adherence to the intervention, participant responsiveness, quality of delivery, and contextual barriers.

RESULTS: A total of 11,029 participants were enrolled in this study, of whom 9983 (90.5%) had a valid mobile phone number registered in the system. Among these, 32.8% (3276/9983) had a diagnosis of type 2 diabetes, 53.3% (5320/9983) were identified as being at moderate or high risk based on the Finnish Diabetes Risk Score questionnaire, and 13.9% (1387/9983) were pregnant women. Overall, 67.3% (n=5749) opted in to receive messages, with the highest acceptance among those with diabetes (n=2169, 74.3%) and the lowest among at-risk people (n=2935, 62.1%). Message adherence was high: 88.7% (n=5004) of participants received at least the minimum number of educational messages expected, and the mean proportion of messages read per participant was 82.2% (SD 29.8). The dropout rate was low (6.1%) but higher among pregnant participants (14.6%). Message delivery issues mostly included problems with WhatsApp on the mobile phones of participants. Technical challenges, including server overload, were addressed during implementation.

CONCLUSIONS: The WhatsApp-based intervention was feasible and well-received in public primary care settings in Argentina, particularly among people with diabetes. The experience illustrates how a WhatsApp-based intervention can be leveraged to strengthen service delivery in low-resource contexts, while also highlighting the need for further work on integration with electronic health records, tailoring of content to population needs, and strategies to enhance digital inclusion for underserved populations.

PMID:41325602 | DOI:10.2196/81098

JMIR Med Inform. 2025 Dec 1;13:e71617. doi: 10.2196/71617.

ABSTRACT

BACKGROUND: Prolonged hospital stays can lead to inefficiencies in health care delivery and unnecessary consumption of medical resources.

OBJECTIVE: This study aimed to identify key clinical variances associated with prolonged length of stay (PLOS) in clinical pathways using a machine learning model trained on real-world data from the ePath system.

METHODS: We analyzed data from 480 patients with lung cancer (age: mean 68.3, SD 11.2 years; n=263, 54.8% men) who underwent video-assisted thoracoscopic surgery at a university hospital between 2019 and 2023. PLOS was defined as a hospital stay exceeding 9 days after video-assisted thoracoscopic surgery. The variables collected between admission and 4 days after surgery were examined, and those that showed a significant association with PLOS in univariate analyses (P<.01) were selected as predictors. Predictive models were developed using sparse linear regression methods (Lasso, ridge, and elastic net) and decision tree ensembles (random forest and extreme gradient boosting). The data were divided into derivation (earlier study period) and testing (later period) cohorts for temporal validation. The model performance was assessed using the area under the receiver operating characteristic curve, Brier score, and calibration plots. Counterfactual analysis was used to identify key clinical factors influencing PLOS.

RESULTS: A 3D heatmap illustrated the temporal relationships between clinical factors and PLOS based on patient demographics, comorbidities, functional status, surgical details, care processes, medications, and variances recorded from admission to 4 days after surgery. Among the 5 algorithms evaluated, the ridge regression model demonstrated the best performance in terms of both discrimination and calibration. Specifically, it achieved area under the receiver operating characteristic curve values of 0.84 and 0.82 and Brier scores of 0.16 and 0.17 in the derivation and test cohorts, respectively. In the final model, a range of variables, including blood tests, care, patient background, procedures, and clinical variances, were associated with PLOS. Among these, particular emphasis was placed on clinical variances. Counterfactual analysis using the ridge regression model identified 6 key variables strongly linked to PLOS. In order of impact, these were abnormal respiratory sounds, postoperative fever, arrhythmia, impaired ambulation, complications after drain removal, and pulmonary air leaks.

CONCLUSIONS: A machine learning-based model using ePath data effectively identified critical variances in the clinical pathways associated with PLOS. This automated tool may enhance clinical decision-making and improve patient management.

PMID:41325598 | DOI:10.2196/71617

JMIR Mhealth Uhealth. 2025 Dec 1;13:e71625. doi: 10.2196/71625.

ABSTRACT

BACKGROUND: Digital health tools, such as mobile apps and wearable devices, have been widely adopted to support self-management of health behaviors. However, user engagement remains inconsistent, particularly among populations with varying BMI. While digital health technologies have the potential to promote healthier behaviors, little is known about how psychological and behavioral factors interact with BMI to influence use patterns.

OBJECTIVE: This study aimed to explore the relationship between BMI and digital health technology use and to examine how factors such as health awareness, self-efficacy, and health motivation contribute to technology engagement.

METHODS: A cross-sectional online survey was conducted from January 2024 to April 2024. A total of 184 valid questionnaire participants were included in this study. The questionnaire was measured on a 5-point Likert scale. Descriptive statistics, chi-square tests, and multiple regression analyses were applied.

RESULTS: Of the participants, 38.6% (71/184) had a BMI<24 kg/m², 42.4% (78/184) had a BMI between 24 and 29.9 kg/m², and 19% (35/184) had a BMI≥30 kg/m². Significant BMI differences were observed based on sex (P<.001) and age (P<.001) but not based on prior digital health tool use. Use rates for Bluetooth or Wi-Fi devices, wearables, and mobile apps were 32.1% (59/184), 38.6% (71/184), and 39.1% (72/184), respectively. A negative correlation between BMI and mobile app use frequency was identified (P=.02). Multiple regression analysis indicated that health motivation significantly predicted digital health use (P<.001), whereas health awareness, lifestyle, and self-efficacy did not.

CONCLUSIONS: Individuals with higher BMI reported a lower frequency of digital health tool use, potentially due to lower health motivation in the studied population. Health motivation was the strongest predictor of digital health engagement. Integrating personalized medical records into apps may enhance health motivation, thereby improving user engagement and promoting healthier behaviors in individuals with higher BMI.

PMID:41325596 | DOI:10.2196/71625

JCO Clin Cancer Inform. 2025 Dec;9:e2500223. doi: 10.1200/CCI-25-00223. Epub 2025 Dec 1.

ABSTRACT

PURPOSE: Tobacco use is a major risk factor for diseases such as cancer. Granular quantitative details of smoking (eg, pack years and years since quitting) are essential for assessing disease risk and determining eligibility for lung cancer screening (LCS). However, existing natural language processing (NLP) tools struggle to extract detailed quantitative smoking data from clinical narratives.

METHODS: We cross-validated four pretrained Bidirectional Encoder Representations from Transformers (BERT)-based models-BERT, BioBERT, ClinicalBERT, and MedBERT-by fine-tuning them on 90% of 3,261 sentences mentioning smoking history to extract six quantitative smoking history variables from clinical narratives. The model with the highest cross-validated micro-averaged F1 scores across most variables was selected as the final SmokeBERT model and was further fine-tuned on the 90% training data. Model performance was evaluated on a 10% holdout test set and an external validation set containing 3,191 sentences.

RESULTS: ClinicalBERT was selected as the final model based on cross-validation and was fine-tuned on the training data to create the SmokeBERT model. Compared with the state-of-the-art rule-based NLP model and the Generative Pre-trained Transformer Open Source Series 20 billion parameter model, SmokeBERT demonstrated superior performance in smoking data extraction (overall F1 score, holdout test: 0.97 v 0.88-0.90; external validation: 0.86 v 0.72-0.79) and in identifying LCS-eligible patients (97% v 59%-97% for ≥20 pack-years and 100% v 60%-84% for ≤15 years since quitting).

CONCLUSION: We developed SmokeBERT, a fine-tuned BERT-based model optimized for extracting detailed quantitative smoking histories. Future work includes evaluating performance on larger clinical data sets and developing a multilingual, language-agnostic version of SmokeBERT.

PMID:41325572 | DOI:10.1200/CCI-25-00223

Pain. 2025 Nov 25. doi: 10.1097/j.pain.0000000000003843. Online ahead of print.

ABSTRACT

Neuropathic chronic low-back pain (neuCLBP) is associated with worse clinical outcomes compared with non-neuropathic or axial CLBP (non-neuCLBP) and has limited effective nonsurgical treatment options, reflecting poor understanding of its underlying pathophysiology. In this study, we compared neuCLBP and non-neuCLBP patients using standardized clinical phenotyping of the neuropathic component alongside multimodal brain functional magnetic resonance imaging (fMRI). We hypothesized that, consistent with the definition of neuropathic pain as pain arising from injury to the somatosensory nervous system, neuCLBP patients would exhibit reduced thalamic volume and/or altered thalamic shape, reduced primary somatosensory cortex (S1) thickness, and altered resting-state functional connectivity of these structures compared with non-neuCLBP patients and pain-free healthy controls. Consistent with previous literature, we observed that neuCLBP patients (n = 28) presented with more severe clinical symptoms than non-neuCLBP patients (n = 28). Structurally, neuCLBP patients exhibited extensive differences in thalamic shape but no significant differences in thalamic volume or S1 gray matter thickness. By contrast, by examining resting-state thalamic connectivity gradient maps, we found that non-neuCLBP patients exhibited the most pronounced alterations in these gradients. This study is the first to combine multimodal fMRI with rigorous, standardized phenotyping to investigate neuCLBP. While our results may be influenced by greater symptom severity in the neuCLBP patients, they indicate that these patients may display distinct central plasticity patterns. The findings also highlight the importance of distinguishing between these clinical phenotypes to reduce heterogeneity in future studies.

PMID:41325555 | DOI:10.1097/j.pain.0000000000003843