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ABSTRACT Introduction Methods Results Conclusions Complete blood count is the most common, basic test requisitioned in hematology. The normal reference ranges of hematological parameters are required owing to variable socioeconomic, environmental, and genetic factors in populations. The current study determines the reference ranges of the healthy Indian donor population of a high socioeconomic group.The study was conducted in the Department of Transfusion Medicine at a tertiary care hospital in India and included 4098 individuals, aged 18–65 years coming for voluntary blood donation from July 2021 to October 2022. Blood samples were collected in K2EDTA, analyzed on the Sysmex XN–31 hematology analyzer, and using statistical tools, the normal reference ranges were calculated.The reference ranges for hemoglobin (HB) (137–185 g/L), WBC (5.1–1.7 × 109/L), platelet count (115.6–370.0 × 109/L) were noted. No statistically significant changes were observed in different age groups. There were gender‐wise differences noted in nearly all parameters. The HB and hematocrit (HCT) range was slightly higher in other Indian and other Asian populations with comparable values with the Chinese, Korean populations, and Western populations; RBC parameters were overall comparable with minor differences; the WBC count was higher than the other Indian and Asian populations particularly the upper limit of lymphocyte and monocyte; and the range of platelet counts had a comparable upper limit with all populations and had the lowest lower value in males in our study, which was comparable to only the Chinese population.It is concluded that reference ranges of common parameters were calculated with minor changes noted in all hematological parameters on comparing with other Indian, Asian population, and Western data. [ABSTRACT FROM AUTHOR]

Background and Objectives: Malaria continues to be a significant public health concern in India, with several regions experiencing endemicity and sporadic outbreaks. The prevalence of malaria in blood donors, in India, varies between 0.02% and 0.07%. Common techniques to screen for malaria, in blood donors and patients, include microscopic smear examination and rapid diagnostic tests (RDTs) based on antigen detection. The aim of this study was to evaluate a new fully automated analyser, XN‐31, for malaria detection, as compared with current practice of using RDT. Materials and Methods: Cross‐sectional analytical study was conducted to evaluate clinical sensitivity and specificity of new automated analyser XN‐31 among blood donors' samples and clinical samples (patients with suspicion of malaria) from outpatient clinic collected over between July 2021 and October 2022. No additional sample was drawn from blood donor or patient. All blood donors and patients' samples were processed by malaria rapid diagnostic test, thick‐smear microscopy (MIC) and the haematology analyser XN‐31. Any donor blood unit incriminated for malaria was discarded. Laboratory diagnosis using MIC was considered the 'gold standard' in the present study. Clinical sensitivity and specificity of XN‐31 were compared with the gold standard. Results: Fife thousand and five donor samples and 82 diagnostic samples were evaluated. While the clinical sensitivity and specificity for donor samples were 100%, they were 72.7% and 100% for diagnostic samples. Conclusion: Automated haematology analysers represent a promising solution, as they can deliver speedy and sensitive donor malaria screening assessments. This method also has the potential to be used for pre‐transfusion malaria screening along with haemoglobin estimation. [ABSTRACT FROM AUTHOR]

Healthcare IoT, a fast-growing field, could revolutionize patient monitoring and intervention. This interconnection raises new security concerns, requiring real-time anomaly detection to protect patient data and device integrity. This study presents a novel security framework that uses combination of Hidden Markov Models (HMM) and Support Vector Machine (SVM) to detect anomalies in real-time healthcare IoT environments with high accuracy. The framework prioritizes real-time strength and efficiency. Sensor data from wearables, medical devices, and other IoT devices is carefully segmented into time intervals. Features are carefully derived from each segment, including statistical summaries, patterns, and frequency domain characteristics. Feature engineering is essential for accurate anomaly detection. Integrating HMM and SVM capabilities is the framework's core. HMM accurately represent concealed states within divided data sequences and analyze patterns of change over time. After that, an independent-trained SVM examines each state. Using proximity to a decision hyperplane in feature space, this SVM can classify data points as normal or anomalous. This method augments HMM temporal capabilities with SVM classification efficiency, increasing sensitivity to anomalous patterns and reducing false positives. The framework's exceptional performance is shown by extensive evaluations on the PhysioNet Challenge 2017 dataset, which includes diverse ECG recordings with labeled anomalies. HMM-SVM outperforms Naive Bayes, LSTM, and Random Forest with 98.66% accuracy. The framework also has high precision, recall, and F1-score, indicating a refined ability to detect real anomalies and reduce false alarms. The framework prioritizes real-time understanding and application alongside its remarkable precision. HMM-SVMs reveal hidden data state changes, revealing context and possible causes of anomalies. Modular design and efficient algorithms enable seamless integration of real-time functionality in low-resource IoT devices, enabling quick and effective security responses. To conclude, this study introduces an HMM-SVM framework for fast Healthcare IoT abnormality detection. The framework emphasizes comprehensibility and real-time applicability while achieving high accuracy. This framework can protect patient data, improve device security, and create a more reliable healthcare IoT ecosystem. [ABSTRACT FROM AUTHOR]

The rising occurrence of long-term illnesses requires inventive and effective healthcare solutions, and the incorporation of Internet of Things (IoT) technologies holds significant potential in revolutionizing conventional medical monitoring. This study presents an innovative method called Adaptive Federated Learning for Chronic Disease Prediction (AFL-CDP), which is specifically designed for real-time medical Internet of Things (IoT) applications. The main objective is to enhance both privacy and accuracy in the surveillance of chronic diseases. AFLCDP utilizes federated learning, a decentralized approach to machine learning that allows for model training on multiple edge devices without the need to transfer raw data to a central server. This not only mitigates privacy concerns related to sensitive medical data but also improves the precision of predictive models by assimilating information from various data sources. The AFL-CDP adaptability enables the ongoing improvement of the predictive model using changing patient data, resulting in personalized and timely forecasts for chronic diseases. In order to improve privacy in IoT devices with limited resources, the study integrates the utilization of SPECK, an advanced technique for preserving privacy. SPECK utilizes secure aggregation and encryption mechanisms to safeguard patient data throughout the federated learning process, guaranteeing confidentiality while preserving the integrity of the model. Ensuring data security and patient privacy are of utmost importance, particularly in the field of medical IoT. The proposed methodology is assessed using a dataset that consists of real-time medical Internet of Things (IoT) data for the purpose of monitoring chronic diseases. The model's performance is evaluated using the Area Under the Curve (AUC) accuracy metric, and AFLCDP achieves an impressive AUC accuracy of 94.37%. This showcases the efficacy of the federated learning framework in capturing the fundamental patterns in varied and decentralized healthcare data. To summarize, this study presents an innovative and strong approach for realtime medical Internet of Things (IoT) applications, highlighting the significance of privacy and precision in monitoring chronic diseases. The combination of AFL-CDP and SPECK offers a thorough method that not only satisfies the strict privacy demands of healthcare data but also achieves a high level of predictive precision, establishing the basis for enhanced patient results and personalized healthcare interventions. [ABSTRACT FROM AUTHOR]

Inflammatory myofibroblastic tumor (IMT) of lung is a rare tumor, accounting for ~0.7% of all lung tumors with varied clinical and radiological presentations. The origin of this tumor is unknown but some studies suggest that it might be a true neoplasm as some mutations on chromosome 2p23 of anaplastic lymphoma kinase (ALK) have been found to be related to this tumor. The morphology of IMT is quite vague and the histopathological diagnosis is predominantly given on excision specimens; in fact, only 6.3% of cases are diagnosed based on analysis of biopsy specimens alone. We illustrate a case of IMT diagnosed in a young male on core biopsy, where the case presented with a large tumor in the lung with metastases to multiple sites that was hence unresectable. Post 3 months of treatment with Crizotinib, there was significant reduction in the tumor size. Another interesting finding was that the ALK immunostain, which helped immensely in the diagnosis, was appreciated better on the Ventana platform rather than on the Dako platform. [ABSTRACT FROM AUTHOR]