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Blockchain technology has gained considerable attention, with an escalating interest in a plethora of numerous applications, ranging from data management, financial services, cyber security, IoT, and food science to healthcare industry and brain research. There has been a remarkable interest witnessed in utilizing applications of blockchain for the delivery of safe and secure healthcare data management. Also, blockchain is reforming the traditional healthcare practices to a more reliable means, in terms of effective diagnosis and treatment through safe and secure data sharing. In the future, blockchain could be a technology that may potentially help in personalized, authentic, and secure healthcare by merging the entire real-time clinical data of a patient’s health and presenting it in an up-to-date secure healthcare setup. In this paper, we review both the existing and latest developments in the field of healthcare by implementing blockchain as a model. We also discuss the applications of blockchain, along with the challenges faced and future perspectives.

Single-cell proteomics can reveal cellular phenotypic heterogeneity and cell-specific functional networks underlying biological processes. Here, we present a streamlined workflow combining microfluidic chips for all-in-one proteomic sample preparation and data-independent acquisition (DIA) mass spectrometry (MS) for proteomic analysis down to the single-cell level. The proteomics chips enable multiplexed and automated cell isolation/counting/imaging and sample processing in a single device. Combining chip-based sample handling with DIA-MS using project-specific mass spectral libraries, we profile on average ~1,500 protein groups across 20 single mammalian cells. Applying the chip-DIA workflow to profile the proteomes of adherent and non-adherent malignant cells, we cover a dynamic range of 5 orders of magnitude with good reproducibility and <16% missing values between runs. Taken together, the chip-DIA workflow offers all-in-one cell characterization, analytical sensitivity and robustness, and the option to add additional functionalities in the future, thus providing a basis for advanced single-cell proteomics applications. Single-cell proteomics is an emerging approach to characterize cell-to-cell differences. Here, the authors develop chips that enable complete proteomic sample processing down to the single-cell level and integrate them with DIA-MS into a streamlined single-cell proteomics workflow.

Skin cancer, particularly melanoma, has remained a severe issue for many years due to its increasing incidences. The rising mortality rate associated with melanoma demands immediate attention at early stages to facilitate timely diagnosis and effective treatment. Due to the similar visual appearance of malignant tumors and normal cells, the detection and classification of melanoma are considered to be one of the most challenging tasks. Detecting melanoma accurately and promptly is essential to diagnosis and treatment, which can contribute significantly to patient survival. A new dataset, Nailmelonma, is presented in this study in order to train and evaluate various deep learning models applying transfer learning for an indigenous nail melanoma localization dataset. Using the dermoscopic image datasets, seven CNN-based DL architectures (viz., VGG19, ResNet101, ResNet152V2, Xception, InceptionV3, MobileNet, and MobileNetv2) have been trained and tested for the classification of skin lesions for melanoma detection. The trained models have been validated, and key performance parameters (i.e., accuracy, recall, specificity, precision, and F1-score) are systematically evaluated to test the performance of each transfer learning model. The results indicated that the proposed workflow could realize and achieve more than 95% accuracy. In addition, we show how the quantization of such models can enable them for memory-constrained mobile/edge devices. To facilitate an accurate, timely, and faster diagnosis of nail melanoma and to evaluate the early detection of other types of skin cancer, the proposed workflow can be readily applied and robust to the early detection of nail melanoma.

The world is facing an acute water shortage. The present irrigation techniques used in the Hyderabad district, Pakistan, are not demand-driven. The present study was carried out to determine the crop water requirement (CWR), irrigation water requirement (IWR), and irrigation scheduling for major crops grown in the Hyderabad district using the CROPWAT model based on climatic, soil, and crop data. The analysis revealed that the total CWR for the entire growing season for sugarcane, banana, cotton, and wheat were 3,127.0; 2,012.3; 1,073.5; and 418.9 mm, respectively. However, the IWR for sugarcane, banana, cotton, and wheat for the entire growing season was found to be 2,964.0; 1,966.7; 1,052.7; and 407.6 mm, respectively. However, the contribution of rainfall was 163.0, 45.6, 20.8, and 11.3 mm during sugarcane, banana, cotton, and wheat, respectively. The CWR and IWR were higher during the dry season due to high temperatures and low relative humidity. However, the IWR of each crop was low in the initial stage which increased with the growing stage until the peak at the full growth stage. The study recommends the use of CROPWAT to investigate the irrigation water requirements with accuracy.

This study aimed to determine the effects of clove powder and choline on the development and digestibility of Japanese quail. For this study, 200-day-old Japanese quail chicks were assigned randomly into four treatments (I, II, III, IV), with each group having five replicates with ten birds each. Group, I treated simply the basal diet, whereas group II treated 0.5% clove powder together with the standard diet, group III treated 0.5% choline along with the standard diet, and group IV was treated with a blend of 0.5% clove powder and 0.5% choline along with the basal diet. The results showed that group I had the highest feed and water consumption, while group IV had the lowest feed consumption and water consumption was lowest in group II with statistically significant differences in all groups. Weekly weight increase was substantially higher in group IV and lower in group I with statistically significant differences in all groups; similarly, ultimate weight gain was significantly higher in group IV than in group I, groups were statistically significant from one another. In comparison to group I, FCR was better in groups IV, II, and III, all groups were statistically different from each other. Carcass weight and dressing percentage were considerably higher in IV, II, and III groups. Heart weight was substantially higher in treated groups, while liver weight was significantly lower in treated groups. Gizzard weight was observed high in treated groups. In terms of digestibility percentage of dry matter, crude protein and crude fat, the maximum efficiency was obtained in group IV, followed by groups II and III, however, there was a significant difference in all groups. On the basis of above mention, the conclusion could be made that supplementing a normal diet with 0.5% clove powder and 0.5% choline improved feed and water intake, weight gain, carcass quantity and dressing percentage, weight of internal organs, FCR, and the digestibility percentage of dry matter, crude protein and fat in Japanese quails.