Background: Morphological diagnosis of non-Hodgkin lymphoma (NHL) is usually based on lymph node biopsy. Bone marrow biopsy (BMB) is important for staging, and morphology alone can be challenging for subtyping. Immunohistochemistry (IHC) allows a more precise diagnosis and characterization of NHL using monoclonal antibodies. However, there is a need for a minimal panel that can provide maximum information at an affordable cost. Methods: All newly diagnosed cases of B-cell NHL with bone marrow infiltration between 2017 and 2019 were included. BMB was the primary procedure for diagnosing B-cell NHL. Subtyping of lymphomas was performed by immunophenotyping using a panel of monoclonal antibodies on IHC. The primary diagnostic panel of antibodies for B-cell NHL included CD19, CD20, CD79, CD5, CD23, CD10, Kappa, and Lambda. The extended panel of antibodies for further subtyping included CD30, CD45, CD56, Cyclin D1, BCL2, and BCL6. Results: All cases of B-cell NHL were classified into the chronic lymphocytic leukemia (CLL) and non-CLL groups based on morphology and primary IHC panel. In the CLL group, the most significant findings were CD5 expression, CD23 expression, dim CD79 expression, and weak surface immunoglobulin (Ig) positivity. In the non-CLL group, they were CD5 expression, positive or negative CD23 expression, strong CD79 expression, and strong surface Ig expression. An extended panel was used for further subtyping of non-CLL cases, which comprised CD10, Cyclin D1, BCL2, and BCL6. Conclusion We propose a two-tier approach for immunophenotypic analysis of newly diagnosed B-cell NHL cases with a minimum primary panel including CD5, CD23, CD79, Kappa, and Lambda for differentiation into CLLon-CLL group and Kappa and Lambda for clonality assessment. An extended panel may be used wherever required for further subtyping of non-CLL.

In this paper,we reviewed the various advanced technologies and methods that could help patients for measuring adherence of patients.There exist intelligent technologies that are available for measuring medication adherence,including medication event monitoring system (MEMS(R)),smart blister packs,radio frequency identification(RFID) embedded smart drawers,and wisely aware RFID dosage (WARD) system.Utilization of these advanced technologies and systems have aided in enhancing the adherence to a greater extent.For example,MEMS(R) refers to the electronic cap that counts the number of bottles opened,but it can be employed only with bottles.Smart blisters are pharmaceutical packagings that possess the capability of monitoring when a pill or tablet is taken out of its packing.All those intelligent technologies can help in active monitoring of patients regarding adherence and capable of eradicating various medication errors due to which adherence is affected.

Cerebral small vessel disease (cSVD) has a crucial role in lacunar stroke and brain hemorrhages and is a leading cause of cognitive decline and functional loss in elderly patients. Based on underlying pathophysiology, cSVD can be subdivided into amyloidal and non-amyloidal subtypes. Genetic factors of cSVD play a pivotal role in terms of unraveling molecular mechanism. An important pathophysiological mechanism of cSVD is blood-brain barrier leakage and endothelium dysfunction which gives a clue in identification of the disease through circulating biological markers. Detection of cSVD is routinely carried out by key neuroimaging markers including white matter hyperintensities, lacunes, small subcortical infarcts, perivascular spaces, cerebral microbleeds, and brain atrophy. Application of neural networking, machine learning and deep learning in image processing have increased significantly for correct severity of cSVD. A linkage between cSVD and other neurological disorder, such as Alzheimer’s and Parkinson’s disease and non-cerebral disease, has also been investigated recently. This review draws a broad picture of cSVD, aiming to inculcate new insights into its pathogenesis and biomarkers. It also focuses on the role of deep machine strategies and other dimensions of cSVD by linking it with several cerebral and non-cerebral diseases as well as recent advances in the field to achieve sensitive detection, effective prevention and disease management.
Aged ; Amyloid ; Atrophy ; Biomarkers* ; Blood-Brain Barrier ; Brain ; Cerebral Small Vessel Diseases* ; Disease Management ; Endothelium ; Humans ; Intracranial Hemorrhages ; Learning ; Machine Learning* ; Nervous System Diseases ; Neuroimaging ; Stroke, Lacunar ; White Matter