Asian Pacific Association for the Study of the Liver clinical practice guidelines on liver transplantation

Liver transplantation is a highly complex and challenging field of clinical practice. Although it was originally developed in western countries, it has been further advanced in Asian countries through the use of living donor liver transplantation. This method of transplantation is the only available option in many countries in the Asia-Pacific region due to the lack of deceased organ donation. As a result of this clinical situation, there is a growing need for guidelines that are specific to the Asia-Pacific region. These guidelines provide comprehensive recommendations for evidence-based management throughout the entire process of liver transplantation, covering both deceased and living donor liver transplantation. In addition, the development of these guidelines has been a collaborative effort between medical professionals from various countries in the region. This has allowed for the inclusion of diverse perspectives and experiences, leading to a more comprehensive and effective set of guidelines.

Brain image fusion-based tumour detection using grey level co-occurrence matrix Tamura feature extraction with backpropagation network classification

Most challenging task in medical image analysis is the detection of brain tumours, which can be accomplished by methodologies such as MRI, CT and PET. MRI and CT images are chosen and fused after preprocessing and SWT-based decomposition stage to increase efficiency. The fused image is obtained through ISWT. Further, its features are extracted through the GLCM-Tamura method and fed to the BPN classifier. Will employ supervised learning with a non-knowledge-based classifier for picture classification. The classifier utilized Trained databases of the tumour as benign or malignant from which the tumour region is segmented via k-means clustering. After the software needs to be implemented, the health status of the patients is notified through GSM. Our method integrates image fusion, feature extraction, and classification to distinguish and further segment the tumour-affected area and to acknowledge the affected person. The experimental analysis has been carried out regarding accuracy, precision, recall, F-1 score, RMSE and MAP.

An Intelligent Classification System for Cancer Detection Based on DNA Methylation Using ML and Semantic Knowledge in Healthcare

To consistently assess a patient's internal and external wellness and diagnose chronic conditions like cancer, Alzheimer's disease, and cardiovascular disease, wearable sensing devices are being used. Wearable technologies and networking websites have become incredibly common in the medical sector in recent times. The condition of a patient's health can be influenced by a number of factors, including psychological response, emotional stability, and anxiety levels, which can be evaluated using social network analysis based on graph theory-based techniques and these ideas, known as "social network analysis" (SNA) are used to study relationship phenomena. Therefore, numerous uses for SNA in health research are possible, ranging from social science to exact science. For example, it can be used to research cooperative networks of healthcare providers and hazard-prone behaviors, infectious disease transmission, and the spread of initiatives for health promotion and prevention. Recently, a number of machine learning-based healthcare solutions have been proposed to track chronic illnesses utilizing data from social networks and wearable monitoring devices. In our suggested approach, we are using an intelligent system with the assistance of wearable sensors for the classification of cancer based on DNA methylation, an important epigenetic process in the human genome that controls gene expression and has been connected to a number of health issues. A mixed-sampling imbalanced data ensemble classification technique is created with the help of biomedical sensors to address the problem of class imbalance and high dimensionality in the Cancer Genome Atlas (TCGA) massive data. This technique is based on the Intelligent Synthetic Minority Oversampling (SMOTE) algorithm. The false-negative rate significantly rises as a result of this, to give a larger data set, a new minority class sample will be first obtained. The noise created during the sample expansion process is actually any data that has been acquired, preserved, or altered in a way that prevents the system that initially conceived it from accessing or utilizing it. Noisy data boosts the amount of space needed excessively and can also drastically influence the findings of any data collection investigation and therefore can also affect the sample sets of one or the other class, resulting in the class imbalance which acts as a common problem in ML datasets. The Tomek Link method is then used to eliminate this noise, producing a reasonably balanced data set. Each layer selects two random forest structures using the cascading forest structure of the deep forest (GC-Forest) algorithm to increase the generalization ability of the model and create the final classification model. Experiments using DNA methylation data collected by employing biosensors from six tumor patients reveal that the mixed-sampling unbalanced data ensemble classification technique may increase the sensitivity to the minority class while maintaining the majority class's classification accuracy.

Lagophthalmos: An etiological lookout to frame the decision for management

Purpose:

To describe the etiology, clinical profile, duration of lagophthalmos cases and thereby, framing a decision for the management based on the severity of Exposure keratitis (EK), Facial palsy (FP) with each etiology and to describe the outcome of the management options.

Methods:

The method was a prospective review of 120 lagophthalmos cases treated at a single tertiary center from January 2018 to January 2019. The main outcome measures were analysing the association between age, etiology, duration and management of lagophthalmos.

Results:

Of the 120 patients studied, paralytic etiology was noted in 86 and eyelid etiology in 34 patients. The percentage of various lagophthalmos etiology documented were Bell’s palsy (35.83%), lagophthalmos in ICU patients (15%), traumatic facial palsy(FP) (10.80%), stroke associated FP (6.67%), infection associated FP (6.67%), iatrogenic FP, cicatricial lagophthalmos (5%), lagophthalmos post eyelid surgeries (5%), neoplastic FP(3.33%), congenital FP (1.67%), proptosis induced lagophthalmos (1.67%), floppy eyelid syndrome induced lagophthalmos (0.83%) and lid coloboma associated lagophthalmos (0.83%). A statistically significant correlation was noted between exposure keratitis and age, with an increased prevalence age advances. The management showed significant variation with individual etiology, with some etiologies unquestionably requiring surgical management. Surgical management is crucial as the duration of lagophthalmos increases more than 6 weeks, EK involving pupillary axis and poor FP recovery.

Conclusion:

This study concludes that the conservative management was sufficient in all cases when the duration is less than 1 week, Exposure keratitis not involving the pupillary axis (EK< Grade II) and FP with good functional recovery ( FP < Grade III). The predominant causes being Bell’s palsy, lagophthalmos in ICU patients and vascular FP. Whereas, cases with poor functional recovery of facial palsy(FP) and permanent eyelid deformation require definitive surgical management like Traumatic FP & cicatricial lagophthalmos.

Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure

Mesothelioma is a dangerous, violent cancer, which forms a protecting layer around inner tissues such as the lungs, stomach, and heart. We investigate numerous AI methodologies and consider the exact DM conclusion outcomes in this study, which focuses on DM determination. K-nearest neighborhood, linear-discriminant analysis, Naive Bayes, decision-tree, random forest, support vector machine, and logistic regression analyses have been used in clinical decision support systems in the detection of mesothelioma. To test the accuracy of the evaluated categorizers, the researchers used a dataset of 350 instances with 35 highlights and six execution measures. LDA, NB, KNN, SVM, DT, LogR, and RF have precisions of 65%, 70%, 92%, 100%, 100%, 100%, and 100%, correspondingly. In count, the calculated complication of individual approaches has been evaluated. Every process is chosen on the basis of its characterization, exactness, and calculated complications. SVM, DT, LogR, and RF outclass the others and, unexpectedly, earlier research.

Sonocatalytic degradation of malachite green oxalate by a semiconductor metal oxide nanocatalyst

Advanced Oxidation Process (AOP) technologies are considered to be better technique for the degradation or mineralization of many recalcitrant compounds and pollutants. In the present study heterogeneous sonocatalytic degradation of a model organic compound such as Malachite green oxalate (MGO) was carried out in the aqueous phase. Zinc oxide nanorods were prepared by precipitation method employing zinc acetates as precursors and were characterized by FT-IR, XRD, FE-SEM and EDAX analysis. Degradation of MGO in the aqueous phase was studied in detail under the sonocatalytic process. Effects of pH, dye concentration, oxidant concentration, kinetics and effect of electrolytes on dye degradation were carried out to check the efficiency of the sonocatalyst. Effect of energy input on the degradation processes was also investigated. The degradation of dye molecules were monitored by UV-visible spectrophotometer and Chemical Oxygen demand (COD). The dye molecules were readily degraded at above 90% in the pH range 5.0-7.0 under ultrasound with zinc oxide nanorods. The interference of electrolytes like NaCl, KCl, Na₂CO₃, NaHCO₃ and MgSO₄ on the degradation of dye molecules were also studied on the sonocatalytic degradation of MGO. From the kinetic studies it was observed that at lower initial concentration of dye molecules the degradation efficiency was above 90%. The rate of the reaction decreased on increasing the initial dye concentrations of the dye molecules. It was observed that the complete mineralization of dye molecules was achieved without the formation of toxic by-products. The reusability of the catalyst also showed the effective degradation of the dye molecules up to five cycles without loss of the catalytic activities.

Solid–liquid interfacial energy as a tool to estimate shifts in isoelectric points of adsorbed proteins on solid surfaces

This work reports the estimation of isoelectric points (pIs) of adsorbed amino acids and proteins on solid surfaces in the pH range between 3.5-11.0 from a measurement of solid/liquid interfacial energy. The values thus obtained are compared with the pIs determined in solution phase by other methods. Both glass and Teflon have been chosen as model solid surfaces. Close agreement between the reference pI values, obtained by the capillary isoelectric focusing and those obtained at solid/liquid interface is observed within an average difference of 0.04-0.08 pH unit when the pIs are above the pI of glass. For systems whose pIs are far away from that of glass (either in the acidic or highly alkaline range), a large shift in the isoelectric point is observed. In case of Teflon the pIs are closer to the reported values than at glass/liquid interface. This could be due to the fact that Teflon being a hydrophobic surface, its surface is dominated by dispersive forces, which may not be seriously affected by pH changes. The shift in the values at solid/liquid interface compared to that in solution have been examined using an 'image charge approach.'