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Abstract Background Left ventricular assist device (LVAD) recipients report symptom improvement but find adjusting to life with the LVAD challenging. These challenges are unique, and existing patient‐reported outcome measures (PROMs) do not reflect their experiences. This study aimed to develop a culturally relevant quality of life PROM for use with LVAD recipients in future research, design evolutions and clinical practice. Methods A three‐stage mixed‐methods approach was used to develop a PROM: stage 1 included group concept mapping (GCM); stage 2 semi‐structured qualitative interviews were conducted with 11 LVAD recipients and 10 clinicians, and a questionnaire was developed using a conceptual framework; and stage 3 used exploratory psychometric analysis of the PROM data using Rasch measurement theory. This paper presents stages 2 and 3. Results The conceptual framework consisted of four key concepts, including general health, life with the LVAD, equipment and clothing and emotional impact. Statements from interviews and GCM were used to create items for the LVAD quality of life (LVAD‐QoL). Cognitive interviews tested face validity and participant comprehension. Forty‐nine participants were recruited from three UK transplant centres. PROM data were collected and analysed using Rasch analysis. Four items displayed misfit; dependency between item sets was the biggest issue (57/485 pairwise differences). After restructuring and dealing with item misfit, the LVAD‐QoL conformed to the Rasch model, supporting the psychometric properties and quality of the LVAD‐QoL. Conclusions Using a mixed‐methods approach ensured the development of a robust and psychometrically sound tool for research, design evolution and clinical practice with LVAD recipients.

Abstract Quick and accurate diagnosis of COVID‐19 is crucial in preventing its transmission. Chest X‐ray (CXR) imaging is often used for diagnosis, however, even experienced radiologists may misinterpret the results, necessitating computer‐aided diagnosis. Deep learning has yielded favourable results previously, but overfitting, excessive variance, and generalization errors may occur due to noise and limited datasets. Ensemble learning can improve predictions by using robust techniques. Therefore, this study, proposes two‐fold strategy that combines advanced and robust algorithms, including DenseNet201, EfficientNetB7, and Xception, to achieve faster and more accurate COVID‐19 detection. Segmented lung images were generated from CXR images using the residual U‐Net model, and two attention‐based ensemble neural networks were used for classification. The COVID‐19 radiography dataset was used to evaluate the proposed approach, which achieved an accuracy of 98.21%, 93.4%, and 89.06% for two, three, and four classes respectively which outperformed previous studies by a significant margin considering COVID, viral pneumonia, and lung opacity simultaneously. Despite the similarity in CXR images of COVID, pneumonia, and lung opacity, the proposed approach achieved 89.06% accuracy, demonstrating its ability to recognize distinguishable features. The developed algorithm is expected to have applications in clinics for diagnosing different diseases using X‐ray images.

ABSTRACT A 12 years‐old, male indigenous dog was presented to Shahedul Alam Qaderi Teaching Veterinary Hospital (SAQTVH) in Chattogram, Bangladesh with a large tumour on its right front leg. The histopathological findings of the tumour were highly suggestive of an alveolar rhabdomyosarcoma (ARMS). The tumour was surgically removed from the dog. While there had been no recurrence for 5 months, the patient died 5 months post‐operatively. The cause of the death and presence of metastasis could not be confirmed as follow‐up investigations were declined by the owner. A histopathological study showed poorly differentiated neoplastic mononuclear cells forming nests in an alveolar pattern. Despite that the limited resources prevented myogenin staining for diagnostic confirmation, the well‐contained, defined appearance of the tumour supports the diagnosis of ARMS. This report highlights the challenges in diagnosing and treating ARMS in indigenous dogs and emphasizes the need for further research and diagnostic markers to improve detection and management.

Abstract Background and Aims Postpartum depression (PPD) is a globally recognized public health concern, yet research focusing on women in urban areas of Bangladesh remains unexplored. This study aimed to address this research gap by investigating the prevalence and associated factors of PPD within the first 2 years after childbirth. Methods A cross‐sectional study was conducted, enrolling 259 women (26.66 ± 4.57 years) residing in urban areas who were attending healthcare delivery centers. Sociodemographic factors, child‐related issues, pregnancy‐related complications, and PPD using the Edinburgh Postnatal Depression Scale (EPDS) were used for data collection. Data analysis involved the application of χ2 tests and logistic regression analysis using SPSS software. Results This study found a 60.6% prevalence of PPD using a cutoff of 10 (out of 30) on the EPDS scale. Logistic regression analysis identified several significant factors associated with PPD, including high monthly family income (odds ratio [OR] = 47.51, 95% confidence interval [CI]: 8.34–270.54, p

Abstract Communication with hand gestures plays a significant role in human‐computer interaction by providing an intuitive and natural way for humans to communicate with machines. Ultrasound‐based devices have shown promising results in contactless hand gesture recognition without requiring physical contact. However, it is challenging to fabricate a densely packed wearable ultrasound array. Here, a stretchable ultrasound array is demonstrated with closely packed transducer elements fabricated using surface charge engineering between pre‐charged 1–3 Lead Zirconate Titanate (PZT) composite and thin polyimide film without using a microscope. The array exhibits excellent ultrasound properties with a wide bandwidth (≈57.1%) and high electromechanical coefficient (≈0.75). The ultrasound array can decipher gestures up to 10 cm in distance by using a contactless triboelectric module and identify materials from the time constant of the exponentially decaying impedance based on their triboelectric properties by utilizing the electrostatic induction phase. The newly proposed metric of the areal‐time constant is material‐specific and decreases monotonically from a highly positive human body (1.13 m2 s) to negatively charged polydimethylsiloxane (PDMS) (0.02 m2 s) in the triboelectric series. The capability of the closely packed ultrasound array to detect material along with hand gesture interpretation provides an additional dimension in the next‐generation human‐robot interaction.

Abstract Background Factors influencing parasitosis in cattle in Bangladesh remain inadequately explored, necessitating a comprehensive investigation for interventions and sustainable livestock farming. Objectives We conducted this study to estimate the prevalence and distribution of gastrointestinal parasites, exploring their intricate relationship with farm management practices across a spectrum of small‐, medium‐, and large‐scale commercial farms. Methods We conducted this study in the Chattogram district of Bangladesh. We collected a total of 189 freshly voided faecal samples from different farms. We recorded the age, breed, milking status, sex, body condition score, and anthelmintic use history of the sampled animals. We processed the samples using the direct smear method, with the identification of one egg per sample being considered positive. Results We estimated the prevalence of gastrointestinal parasite infection in large‐scale (52.1%), medium‐scale (54.5%), and small‐scale farms (70.0%), with statistically significant differences (p ≤ 0.05). Both pregnant and lactating cows, as well as indigenous cattle, were more likely to have gastrointestinal parasites (p ≤ 0.05). The predominant parasites across farms of all sizes were trematodes (Paramphistomum spp. and Schistosomas spp.) and protozoa (Balantidium coli and Coccidia spp.). Conclusion Poor farm management practices, such as no pasture management and inadequate deworming regimens, may contribute to the elevated prevalence and infection load observed on small‐scale farms. The increased parasitosis in previously dewormed animals can be attributed to the development of anthelmintic resistance against gastrointestinal parasites. Implementing proper and effective deworming strategies is crucial to preventing gastrointestinal parasitosis and mitigating the risk of anthelmintic resistance.

Accurate lung cancer detection is vital for timely diagnosis and treatment. This study evaluates the performance of six convolutional neural network (CNN) architectures, ResNet-50, VGG-16, ResNet-101, VGG-19, DenseNet-201, and EfficientNet-B4, using the LIDC-IDRI dataset. Models were assessed both in their base forms and with transfer learning. The dataset consisted of 460 × 460 × 3 pixel images categorized into squamous cell carcinoma (SCC), normal benign, large cell carcinoma (LCC), and adenocarcinoma (ADC). Performance metrics were computed, including accuracy (99.47% for the custom CNN), precision (99.50%), recall (98.37%), AUC (99.98%), and F1-score (98.98%) during training. However, overfitting was observed in the validation phases. Transfer learning models showed better generalization, with DenseNet-201 achieving a top validation accuracy of 96.88% and EfficientNet-B4 of 96.53%. Hyperparameter tuning improved the models’ generalization capabilities, maintaining high accuracy while reducing overfitting. This study highlights the effectiveness of transfer learning, particularly DenseNet-201, in enhancing automated lung cancer detection systems. Future work will focus on expanding datasets and exploring additional augmentation techniques to further refine model performance in clinical settings.

Abstract Atypical presentations may be presented with the common symptoms in Dengue. We, hereby, present a case of Dengue who was admitted in our hospital with the complaints of fever, upper abdominal pain, and vomiting, literally diagnosed as a case of acute pancreatitis.

Abstract Background Wasting develops over a short period and can be reversed with short‐term interventions. The prevalence of wasting typically varies from season to season—becoming higher during the monsoon (June to September) season as compared to the winter (October to January) and summer (February to May) seasons every year in a cyclical fashion. However, to the best of our knowledge, using nationally representative demographic surveys to extensively study the impact of the timing of the survey on the results and trends around wasting has not been done so far. Objectives The goal of this study is to ascertain whether seasonality has an impact on the trend and levels of wasting between NFHS‐3 (2005–2006) and NFHS‐5 (2019–2021). Methods The analysis was based on data on 51,555, 259,627, and 232,920 children under 5 years included in NFHS‐3, NFHS‐4, and NFHS‐5 respectively. Multivariable logistic regression analysis and the predicted probabilities approach were employed to examine the effect of the months of interview on the prevalence of wasting. The analysis was conducted for 9 states of India which had data for comparable months to compute wasting levels. Results We found that at the national level, wasting increased in India by one per cent from NFHS‐3 to NFHS‐4 but declined by 2% from NFHS‐4 to NFHS‐5. The results show that seasonality significantly influenced the prevalence of wasting. It was observed that compared to January, the odds of wasting were particularly higher in summer and monsoon seasons, especially in the month of August across all three rounds, indicating the influence of seasonality in the prevalence of wasting in the country. Discussion The prevalence of wasting in India needs to be interpreted across seasonal changes as seasonality affects many of the variables intrinsically related to child health and nutritional status.

Because of the limited technical properties of their native forms, oils and fats are frequently blended to achieve the desired textural and oxidative properties. In this study, canola and palm oil blends were prepared in nine different proportions: B1 (90 : 10), B2 (80 : 20), B3 (70 : 30), B4 (60 : 40), B5 (50 : 50), B6 (40 : 60), B7 (30 : 70), B8 (20 : 80), and B9 (10 : 90). Pure palm oil (PO) and canola oil (CO) were used as the controls. All blends were assessed for physicochemical properties, fatty acid composition, heat treatment, and polymer content. The results indicated negative cold tests only for B1 and B2 blends with 10 and 20% PO, respectively. Iodine value decreased with increasing palm oil concentration and was lowest (62.03 ± 0.526) for blend B9, i.e., 90% PO. The fatty acid profile indicated more saturated fatty acids and a higher percentage of oleic acid in PO than in CO. The fatty acid profile values of blends B1–B9 were between those of the pure PO and CO. Linoleic and linolenic acids were more in blends B1–B9 than those in pure PO. The polymer content of PO (7.17%) was found to be lower than that of CO (10.32%) after 60 h of heating at 180°C. In addition, biologically active substances (BASs), which could be formed during the frying process, were tested by measuring the inhibition zone of E. coli growth. PO retarded BAS formation. The blended palm or canola oils resulted in better stability and increased organoleptic characteristics and hence can be suitable as economical and healthy alternatives to pure palm or canola oil.

Abstract Background The Medicare population is increasing while the prevalence of obesity remains high. Bariatric surgery is the most efficacious treatment of obesity and its comorbidities. The objective of this investigation was to assess trends in utilization, readmission, mortality, and cost of bariatric surgery in the Medicare population. Methods Utilizing the Medicare Provider Analysis and Review database, patients with clinically severe obesity undergoing laparoscopic Roux‐en‐Y gastric bypass (RYGB), laparoscopic sleeve gastrectomy (SG), and laparoscopic adjustable gastric banding (LAGB) from 2011–2015 were identified. Trends in procedure selection, readmissions, mortality, and cost were examined. A multivariable logistic regression analysis to evaluate factors associated with readmission and mortality was performed. Results Of the 73,718 patients identified, 53,949 (73%) of patients were enrolled in Medicare due to disability, 19,191 (26%) due to age, and 578 (

In this paper, we develop a deep learning model using back propagation neural network (BPNN) that helps to obtain maximum power point. This deep learning model aims to maximise the output power from the solar grids when the panels are connected with the boost converter under different variable load conditions. BPNN-DL enables the prediction of reference voltage at different weather conditions for severing the various output power that ensures maximum power with stable output voltage. The proposed BPNN-DL is tested under different conditions to estimate the robustness of the modules under internal/external interferences. The results of the simulation show that the proposed method achieves maximum output power from each panel compared with existing methods in terms of regression analysis on training, testing, and validation.

The purpose of this study was to clarify the role of pattern recognition receptors in Behçet’s disease (BD). The frequencies of several pattern recognition receptors (CD11b, CD11c, CD32, CD206, CD209, and dectin-1) were analyzed in patients with BD by flow cytometry, and cytokine levels, interleukin- (IL-) 18, IL-23, and IL-17A, were compared in plasma. The analysis was performed in active (n=13) and inactive (n=13) stages of BD patients. Rheumatoid arthritis patients (n=19), as a disease control, and healthy control (HC) (n=19) were enrolled. The frequencies of CD11b+ and CD32+ cells were significantly increased in active BD patients compared to HC. Disease severity score was correlated to CD11c+, CD206+, and CD209+ in whole leukocytes and CD11b+, CD11c+, CD206+, CD209+, and Dectin-1+ in granulocytes. The plasma levels of IL-17A were significantly different between HC and active BD. IL-18 showed significant difference between active and inactive BD patients. From this study, we concluded the expressions of several pattern recognition receptors were correlated to the joint symptoms of BD.