Evaluating compliance with the care standard of proactively assessing bone health in patients with diabetes: a pilot audit of practice across Asia by the Asia Pacific Consortium on Osteoporosis (APCO).

This pilot audit explored how bone health is assessed patients with diabetes in diverse centres across Asia. Only 343 of 1092 (31%) audited patients had a bone health assessment, 27% of whom were diagnosed with osteoporosis. Quality improvement strategies are needed to address gaps in patient care in this area. PURPOSE: The Asia Pacific Consortium on Osteoporosis (APCO) Framework outlines clinical standards for assessing and managing osteoporosis. A pilot audit evaluated adherence to clinical standard 4, which states that bone health should be assessed in patients with conditions associated with bone loss and/or increased fracture risk; this report summarises the audit findings in patients with diabetes. A secondary aim was to assess the practicality and real-world use of the APCO bone health audit tool kit. METHODS: Eight centres across Asia participated in the pilot audit, selecting diabetes as the target group. Participants reviewed their practice records for at least 20 consecutively treated patients with the target condition. Questions covered routine investigations, bone health assessment, osteoporosis diagnosis, and patient referral pathways. Data were summarised descriptively. RESULTS: The participants represented public hospitals, university medical centres, and private clinics from India, Malaysia, Pakistan, Singapore, Taiwan, and Vietnam that see an estimated total of 95,000 patients with diabetes per year. Overall, only 343 of 1092 audited patients (31%) had a bone health assessment. Osteoporosis was subsequently diagnosed in 92 of 343 (27%) patients. CONCLUSION: Bone health was not assessed in most patients with diabetes. The results provide insight into current practices across diverse Asian centres and demonstrate the practical value of the audit tool kit. Participant feedback has been used to improve the tool kit. Results of this pilot audit are being used in the respective centres to inform quality improvement projects needed to overcome the gap in patient care.

Fiber Optic Sensor Coated with Multiple Layers of Hexagonal Boron Nitride Nanosheets (BNNS) for the Detection of Volatile Organic Compounds.

Nowadays, volatile organic compound (VOC) detection is imperative to ensure environmental safety in industry and indoor environments, as well as to monitor human health in medical diagnosis. Gas sensors with the best sensor response, selectivity, and stability are in high demand. Simultaneously, the advancement of nanotechnology facilitates novel nanomaterial-based gas sensors with superior sensor characteristics and low power consumption. Recently, boron nitride, a 2D material, has emerged as an excellent candidate for gas sensing and demonstrated exceptional sensing characteristics for new-generation gas sensing devices. Herein, ultrathin porous boron nitride nanosheets (BNNSs) with large lateral sizes were synthesized using a facile synthesis approach, and their material characteristics were investigated utilizing a variety of analytical techniques, including X-ray diffraction, Fourier transform infrared spectroscopy, ultraviolet spectroscopy, X-ray photoelectron spectroscopy, and scanning electron microscopy. A BNNS-coated cladding-modified fiber optic sensor (FOS) probe was prepared and employed for VOC (ammonia, ethanol, and acetone) sensing across concentrations varying from 0 to 300 ppm. The BNNSs-coated FOS demonstrated better selectivity toward 300 ppm ammonia, and specifically annealed BNNSs displayed a maximum sensor response of 55% along with a response/recovery times of 15 s/34 s compared to its counterparts. The superior ammonia sensing performances could be attributed to the formation of ultrathin nanosheets and a porous surface with slit-like features in hexagonal boron nitride.

ALBAE feature extraction based lung pneumonia and cancer classification.

Lung cancer is a deadly disease showing uncontrolled proliferation of malignant cells in the lungs. If the lung cancer is detected in early stages, it can be cured before critical stage. In recent years, new technologies have gained much attention in the healthcare industry however, the unpredictable appearance of tumors, finding their presence, determining its shape, size and high discrepancy in medical images are the challenging tasks. To overcome this issue a novel Ant lion-based Autoencoders (ALbAE) model is proposed for efficient classification of lung cancer and pneumonia. Initially Computed Tomography (CT) images are pre-processed using median filters to remove noise artifacts and improving the quality of the images. Consequently, the relevant features such as image edges, pixel rates of the images and blood clots are extracted by ant lion-based autoencoder (ALbAE) technique. Finally, in classification stage, the lung CT images are classified into three different categories such as normal lung, cancer affected lung and pneumonia affected lung using Random forest technique. The effectiveness of the implemented design is estimated by different parameters such as precision, recall, Accuracy and F1-measure. The proposed approach attains 97% accuracy; 98% of recall and F-measure rate is attained through the developed design and the proposed model gains 96% of precision score. Experimental outcomes show that the proposed model performs better than existing SVM, ELM, and MLP in classifying lung cancer and pneumonia.

Feature Selection and Dwarf Mongoose Optimization Enabled Deep Learning for Heart Disease Detection.

Heart disease causes major death across the entire globe. Hence, heart disease prediction is a vital part of medical data analysis. Recently, various data mining and machine learning practices have been utilized to detect heart disease. However, these techniques are inadequate for effectual heart disease prediction due to the deficient test data. In order to progress the efficacy of detection performance, this research introduces the hybrid feature selection method for selecting the best features. Moreover, the missed value from the input data is filled with the quantile normalization and missing data imputation method. In addition, the best features relevant to disease detection are selected through the proposed hybrid Congruence coefficient Kumar-Hassebrook similarity. In addition, heart disease is predicted using SqueezeNet, which is tuned by the dwarf mongoose optimization algorithm (DMOA) that adapts the feeding aspects of dwarf mongoose. Moreover, the experimental result reveals that the DMOA-SqueezeNet method attained a maximum accuracy of 0.925, sensitivity of 0.926, and specificity of 0.918.

A Bilayer SnO2/MoS2-Coated Evanescent Wave Fiber Optic Sensor for Acetone Detection-An Experimental Study.

The need for sensors that measure the acetone content of exhaled breath for diabetes severity has recently increased. Clinical researchers have reported less than 0.8 ppm acetone concentration in the exhaled breath of an average individual, while that for a diabetic patient is higher than 1.8 ppm. This work reports the development of two sets of evanescent wave-based fiber optic sensor coated with SnO2 thin film and bilayer of SnO2/MoS2 to detect different acetone concentrations (0-250 ppm). In each set, we have studied the effect of clad thickness (chemical etch time 5min, 10 min, 15 min, 25 min, 40 min, and complete clad removal) to optimize the clad thickness for a better response. In Set 1, SnO2 thin film was used as the sensing layer, while in Set 2 a bilayer of SnO2 thin film/ MoS2 was used. Enhanced sensor response of ~23.5% is observed in the Set 2 probe with a response and recovery time of ~14 s/~17 s. A SnO2/MoS2-coated sensor prototype is developed using LEDs of different wavelength and intensity detector; its potential to detect different concentrations of acetone is tested. X-ray Diffraction (XRD), Scanning Electron Microscope (SEM), Ultraviolet (UV) Spectroscopy, and Ellipsometry were used to study the structural, morphological and optical properties of the sensing layers. The present study indicates that the SnO2/MoS2-coated sensor has the potential to create a handheld sensor system for monitoring diabetes.

Blistering skin lesion: A new observation of Hottentotta tamulus (white scorpion) sting.

White scorpion (Hottentotta tamulus) stings are an emerging health hazard in northern Sri Lanka and are increasingly recognized particularly during the last decade. The stings are usually harmless; however, fatal cardiovascular sequelae ensues following severe envenomation. It is often difficult to identify this miniature creature and its site of sting due to its nature of habitat and nonspecific local symptoms that develop after the sting. Here, we report a patient who had developed a blistering skin lesion soon after the sting which is not reported in the literature to date by the sting of this particular species. Recognizing it would help the clinicians of this subcontinent to consider scorpion sting as a differential diagnosis for the above presentation in the future.

Canavan disease and the role of N-acetylaspartate in myelin synthesis.

Canavan disease (CD) is an autosomal-recessive neurodegenerative disorder caused by inactivation of the enzyme aspartoacylase (ASPA, EC 3.5.1.15) due to mutations. ASPA releases acetate by deacetylation of N-acetylaspartate (NAA), a highly abundant amino acid derivative in the central nervous system. CD results in spongiform degeneration of the brain and severe psychomotor retardation, and the affected children usually die by the age of 10. The pathogenesis of CD remains a matter of inquiry. Our hypothesis is that ASPA actively participates in myelin synthesis by providing NAA-derived acetate for acetyl CoA synthesis, which in turn is used for synthesis of the lipid portion of myelin. Consequently, CD results from defective myelin synthesis due to a deficiency in the supply of the NAA-derived acetate. The demonstration of the selective localization of ASPA in oligodendrocytes in the central nervous system (CNS) is consistent with the acetate deficiency hypothesis of CD. We have tested this hypothesis by determining acetate levels and studying myelin lipid synthesis in the ASPA gene knockout model of CD, and the results provided the first direct evidence in support of this hypothesis. Acetate supplementation therapy is proposed as a simple and inexpensive therapeutic approach to this fatal disease, and progress in our preclinical efforts toward this goal is presented.