Neuro-Behçet's presentation as cerebral venous thrombosis - A report of two cases and review of the literature.

ABSTRACT: Cerebral venous thrombosis (CVT) is a rare stroke with multiple risk factors. One rare risk factor is Behçet's disease (BD). Out of around 3000 cases at our center in the past 10 years, two cases of BD with CVT were seen. Herein, we report on their clinical symptoms, course, and management. Case 1 was a 18-year-old girl with a history of recurrent skin lesions presenting with encephalopathy syndrome due to CVT, requiring decompression. Despite our best efforts, she developed complications and expired due to sepsis. Case 2 was a 22-year-old male with raised intracranial pressure syndrome and a history of recurrent orogenital ulcers. His evaluation showed retinal vasculitis, papilledema, and bilateral lateral rectus palsy. Both had CVT on neuroimaging and had positivity for human leukocyte antigen-B51. Case 2 responded to the anticoagulation and immunomodulation. Risk factor identification is essential in managing CVT, and planned evaluation (clinical or investigations) plays an important role in identifying rare causes that need specific treatment.

PI3Kγ in B cells promotes antibody responses and generation of antibody-secreting cells.

The differentiation of naive and memory B cells into antibody-secreting cells (ASCs) is a key feature of adaptive immunity. The requirement for phosphoinositide 3-kinase-delta (PI3Kδ) to support B cell biology has been investigated intensively; however, specific functions of the related phosphoinositide 3-kinase-gamma (PI3Kγ) complex in B lineage cells have not. In the present study, we report that PI3Kγ promotes robust antibody responses induced by T cell-dependent antigens. The inborn error of immunity caused by human deficiency in PI3Kγ results in broad humoral defects, prompting our investigation of roles for this kinase in antibody responses. Using mouse immunization models, we found that PI3Kγ functions cell intrinsically within activated B cells in a kinase activity-dependent manner to transduce signals required for the transcriptional program supporting differentiation of ASCs. Furthermore, ASC fate choice coincides with upregulation of PIK3CG expression and is impaired in the context of PI3Kγ disruption in naive B cells on in vitro CD40-/cytokine-driven activation, in memory B cells on toll-like receptor activation, or in human tonsillar organoids. Taken together, our study uncovers a fundamental role for PI3Kγ in supporting humoral immunity by integrating signals instructing commitment to the ASC fate.

Bioengineered sustainable phytofabrication of anatase TiO2 -adorned g-C3N4 nanocomposites and unveiling their photocatalytic potential towards advanced environmental remediation.

The ecologically friendly properties, low-cost, and readily available titanium dioxide (TiO2) materials have made them a subject of considerable interest for numerous promising applications. Anatase TiO2 nanoparticles were synthesized in the current study through the utilization of a hibiscus leaf extract and the advent of TiO2-doped g-C3N4(TiCN) nanocomposites (varying 0.5 mM, 1.0 mM, 1.5 mM, and 2.0 mM) by thermal polymerization. Here, the proposed study utilized multiple analytical techniques, including UV-Vis spectroscopy, a diffraction pattern (XRD), SEM coupled with EDX analysis, TGA, and EPR, to characterize the as-prepared TiO2 nanoparticles and TiCN nanocomposites. BET analysis the adsorption-desorption isotherms of the TiCN(1.5 mM) nanocomposite, the surface area of the prepared nanocomposite is 112.287 m2/g, and the pore size is 7.056 nm. The XPS spectra support the development of the TiCN(1.5 mM) nanocomposite by demonstrating the presence of C and N elements in the nanocomposite in addition to TiO2. HRTEM images where the formation of stacked that indicates a planar, wrinkled graphitic-like structure is clearly visible. The TiCN (1.5 mM) specimen exhibited enhanced morphology, enhanced surface area, greater capacity to take in visible light, and lowered band gap when compared to g-C3N4 following z-scheme heterojunction. The sample denoted as TiCN (1.5 mM) exhibited superior performance in terms of adsorption and photocatalytic activity using rhodamine B and Bisphenol A. Furthermore, the TiCN (1.5 mM) composite exhibited satisfactory stability over four cyclic runs, indicating its potential application in minimizing the impact of organic wastewater contaminants when compared to g-C3N4.

The psychological reality of the learned "p < .05" boundary.

The .05 boundary within Null Hypothesis Statistical Testing (NHST) "has made a lot of people very angry and been widely regarded as a bad move" (to quote Douglas Adams). Here, we move past meta-scientific arguments and ask an empirical question: What is the psychological standing of the .05 boundary for statistical significance? We find that graduate students in the psychological sciences show a boundary effect when relating p-values across .05. We propose this psychological boundary is learned through statistical training in NHST and reading a scientific literature replete with "statistical significance". Consistent with this proposal, undergraduates do not show the same sensitivity to the .05 boundary. Additionally, the size of a graduate student's boundary effect is not associated with their explicit endorsement of questionable research practices. These findings suggest that training creates distortions in initial processing of p-values, but these might be dampened through scientific processes operating over longer timescales.

Stress-mediated polysorbate 20 degradation and its potential impact on therapeutic proteins.

PURPOSE: Polysorbates are the most commonly used surfactants in formulations to stabilize therapeutic proteins against interfacial stresses. Polysorbates can undergo oxidative or enzyme-mediated hydrolytic degradation to produce free fatty acids (FFAs) and subvisible particles in formulations. To determine which product related variables contribute to PS20 degradation, we investigated the effects of storage temperature, formulation, pH, presence of hydrolytic enzymes, and specific fatty acid composition on different grades of PS20 in relation to their PS20 degradation profile and consequently the quality of protein drug products. METHODS: Bevacizumab and T-DM1 were reformulated in the freshly prepared therapeutic protein formulations containing either compendial PS20 or non-compendial PS20 with high % lauric acid and spiked with exogenous esterase or lipase. The release of FFAs and formation of particles were monitored at 4°C and 37°C. Protein quality was assessed for secondary structures, purity, and biological activity. RESULTS: Hydrolytic release of FFAs and formation of subvisible particles were found to be dependent on grades of PS20, types of enzymes used, incubation temperature, and pH. Esterase- or lipase-mediated degradation of PS20 and formation of subvisible particles in drug formulation showed no significant impact on the biological activity and stability of therapeutic proteins against degradation or aggregation. CONCLUSIONS: Our study suggests that degradation of PS20 and formation of FFA particles depend on the fatty acid composition of PS20, types of hydrolytic enzymes, pH, and temperature. The presence of FFA subvisible particles showed no significant impact on the purity and biological activity of the therapeutic proteins under the tested conditions.

Long-term treatment with selective PI3Kδ inhibitor leniolisib in adults with activated PI3Kδ syndrome.

ABSTRACT: Activated phosphoinositide 3-kinase delta (PI3Kδ) syndrome (APDS) is an inborn error of immunity that manifests as immune deficiency and dysregulation; symptoms include frequent infections and lymphoproliferation. In our dose-finding and phase 3 placebo-controlled trials, treatment with the selective PI3Kδ inhibitor leniolisib reduced lymphoproliferation and normalized lymphocyte subsets. Here, we present 6 years of follow-up from the 6 adult patients in the original dose-finding trial receiving leniolisib. We used data from the ongoing open-label extension study, which was supplemented at later time points by investigators, including health-related quality of life (HRQoL) assessed through a clinician-reported questionnaire. We observed improvements in HRQoL: 5 of 6 patients experienced an increase in physical capabilities and socialization, and a decrease in prescribed medications. Immune subsets improved in all patients: mean transitional B-cell levels decreased from 38.17% to 2.47% and the CD4:CD8 T-cell ratio normalized to 1.11. Manifestations seen before and within the first year of leniolisib exposure, such as infections and gastrointestinal conditions, attenuated after year 2, with few new conditions emerging out to year 6. Thrombocytopenia or lymphopenia remained present in half of patients at year 6. Of 83 adverse events through year 5, 90.36% were grade 1; none were grade 4/5 nor deemed leniolisib related. Collectively, we saw an enhancement in HRQoL as well as durable changes in lymphocyte subsets and clinical manifestations, further supporting the use of leniolisib as a long-term therapeutic option for the treatment of APDS. This trial was registered at www.ClinicalTrials.gov as #NCT02859727.

Antimicrobial resistance in bacterial wound, skin, soft tissue and surgical site infections in Central, Eastern, Southern and Western Africa: A systematic review and meta-analysis.

Antimicrobial resistance (AMR) is a major global threat and AMR-attributable mortality is particularly high in Central, Eastern, Southern and Western Africa. The burden of clinically infected wounds, skin and soft tissue infections (SSTI) and surgical site infections (SSI) in these regions is substantial. This systematic review reports the extent of AMR from sampling of these infections in Africa, to guide treatment. It also highlights gaps in microbiological diagnostic capacity. PubMed, MEDLINE and Embase were searched for studies reporting the prevalence of Staphylococcus aureus, Eschericheria coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii in clinically infected wounds, SSTI and SSI in Central, Eastern, Southern or Western Africa, and studies reporting AMR from such clinical isolates. Estimates for proportions were pooled in meta-analyses, to estimate the isolation prevalence of each bacterial species and the proportion of resistance observed to each antibiotic class. The search (15th August 2022) identified 601 articles: 59 studies met our inclusion criteria. S. aureus was isolated in 29% (95% confidence interval [CI] 25% to 34%) of samples, E. coli in 14% (CI 11% to 18%), K. pneumoniae in 11% (CI 8% to 13%), P. aeruginosa in 14% (CI 11% to 18%) and A. baumannii in 8% (CI 5% to 12%). AMR was high across all five species. S. aureus was resistant to methicillin (MRSA) in >40% of isolates. E. coli and K. pneumoniae were both resistant to amoxicillin-clavulanic acid in ≥80% of isolates and resistant to aminoglycosides in 51% and 38% of isolates respectively. P. aeruginosa and A. baumannii were both resistant to anti-pseudomonal carbapenems (imipenem or meropenem) in ≥20% of isolates. This systematic review found that a large proportion of the organisms isolated from infected wounds, SSTI and SSI in Africa displayed resistance patterns of World Health Organisation (WHO) priority pathogens for critical or urgent antimicrobial development.

Extraction of persistent lagrangian coherent structures for the pollutant transport prediction in the Bay of Bengal.

Lagrangian Coherent Structures (LCS) are the hidden fluid flow skeletons that provide meaningful information about the Lagrangian circulation. In this study, we computed the monthly climatological LCSs (cLCS) maps utilizing 24 years (1994-2017) of HYbrid Coordinate Ocean Model (HYCOM) currents and ECMWF re-analysis winds in the Bay of Bengal (BoB). The seasonal reversal of winds and associated reversal of currents makes the BoB dynamic. Therefore, we primarily aim to reveal the cLCSs associated with seasonal monsoon currents and mesoscale (eddies) processes over BoB. The simulated cLCS were augmented with the complex empirical orthogonal functions to confirm the dominant lagrangian transport pattern features better. The constructed cLCS patterns show a seasonal accumulation zone and the transport pattern of freshwater plumes along the coastal region of the BoB. We further validated with the satellite imagery of real-time oil spill dispersion and modelled oil spill trajectories that match well with the LCS patterns. In addition, the application of cLCSs to study the transport of hypothetical oil spills occurring at one of the active oil exploration sites (Krishna-Godavari basin) was described. Thus, demonstrated the accumulation zones in the BoB and confirmed that the persistent monthly cLCS maps are reasonably performing well for the trajectory prediction of pollutants such as oil spills. These maps will help to initiate mitigation measures in case of any occurrence of oil spills in the future.

Superior Ophthalmic Vein Thrombosis Associated With Asymptomatic COVID-19 Infection.

Superior ophthalmic vein thrombosis is a rare condition scarcely described in clinical literature with potentially severe consequences including permanent vision loss. This report details the case of a 70-year-old woman who presented with acute binocular horizontal diplopia, relative proptosis of the OD by 4 mm, and pain OD. On exam, visual acuity was 20/20 OD and 20/30 OS with full extraocular movements. CT revealed proptosis OD with a thrombosed superior ophthalmic varix. Evaluation for etiology of hypercoagulability was unremarkable, although the patient did have an asymptomatic COVID-19 infection 1 month prior. To the authors' knowledge, this is the first reported case of superior ophthalmic vein thrombosis secondary to an asymptomatic COVID-19 infection.

Effect of vascular geometry on haemodynamic changes in a carotid artery bifurcation using numerical simulation.

OBJECTIVES: The geometry of carotid bifurcation is a crucial contributing factor to the localization of atherosclerotic lesions. Currently, studies on carotid bifurcation geometry are limited to the region near to bifurcation. This study aimed to determine the influence of carotid bifurcation geometry on the blood flow using numerical simulations considering magnitude of haemodynamic parameters in the extended regions of carotid artery. METHODS: In the present study, haemodynamic analysis is carried out using the non-Newtonian viscosity model for patient-specific geometries consisting of both Left and Right carotid arteries. A 3D patient-specific geometric model is generated using MIMICS, and a numerical model is created using ANSYS. RESULTS: The results obtained from patient-specific cases are compared. The influence of geometric features such as lumen diameter, bifurcation angle, and tortuosity on the haemodynamics parameters such as velocity, WSS, pressure, Oscillatory Shear Index (OSI), and Time-Averaged Wall Shear Stress (TAWSS) are compared. CONCLUSION: The results demonstrate significant changes in the flow regime due to the geometric shape of the carotid artery. It is observed that the lower value of TAWSS occurs near the bifurcation region and carotid bulb region. In addition, the higher value of the (OSI) is observed in the Internal Carotid Artery (ICA) and the tortuous carotid artery region. However, it is also observed that apart from the bifurcation angle, other factors, such as tortuosity and area ratio, play a significant role in the flow dynamics of the carotid artery.

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model.

Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and 'speech records' of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization.

Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the "HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures.

Metformin Effects on SHIP2, AMPKs and Gut Microbiota: Recent Updates on Pharmacology.

INTRODUCTION: Metformin, a biguanide on the WHO's list of essential medicines has a long history of 50 years or more in treating hyperglycemia, and its therapeutic saga continues beyond diabetes treatment. Glucoregulatory actions are central to the physiological effects of metformin; surprisingly, the precise mechanism with which metformin regulates glucose metabolism is not thoroughly understood yet. METHOD: The main aim of this review is to explore the recent implications of metformin in hepatic gluconeogenesis, AMPKs, and SHIP2 and subsequently to elucidate the metformin action across intestine and gut microbiota. We have searched PubMed, google scholar, Medline, eMedicine, National Library of Medicine (NLM), clinicaltrials.gov (registry), and ReleMed for the implications of metformin with its updated role in AMPKs, SHIP2, and hepatic gluoconeogenesis, and gut microbiota. In this review, we have described the efficacy of metformin as a drug repurposing strategy in modulating the role of AMPKs and lysosomal-AMPKs, and controversies associated with metformin. RESULT: Research suggests that biguanide exhibits hormetic effects depending on the concentrations used (micromolar to millimolar). The primary mechanism attributed to metformin action is the inhibition of mitochondrial complex I, and subsequent reduction of cellular energy state, as observed with increased AMP or ADP ratio, thereby metformin can also activate the cellular energy sensor AMPK to inhibit hepatic gluconeogenesis. However, new mechanistic models have been proposed lately to explain the pleiotropic actions of metformin; at low doses, metformin can activate lysosomal-AMPK via the AXIN-LKB1 pathway. Conversely, in an AMPK-independent mechanism, metformin-induced elevation of AMP suppresses adenylate cyclase and glucagon-activated cAMP production to inhibit hepatic glucose output by glucagon. Metformin inhibits mitochondrial glycerophosphate dehydrogenase; mGPDH, and increases the cytosolic NADH/NAD+, affecting the availability of lactate and glycerol for gluconeogenesis. Metformin can inhibit Src homology 2 domain-containing inositol 5-phosphatase 2; SHIP2 to increase the insulin sensitivity and glucose uptake by peripheral tissues. CONCLUSION: In addition, new exciting mechanisms suggest the role of metformin in promoting beneficial gut microbiome and gut health; metformin regulates duodenal AMPK activation, incretin hormone secretion, and bile acid homeostasis to improve intestinal glucose absorption and utilization.

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques.

Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm ("DPSO-GA") for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation.

In vitro antioxidant activity, antidiabetic activity and in silico docking studies of 3,3',4',5,7-pentahydroxyflavone and stigmasta-5,22-dien-3ß-ol isolated from aerial parts of Euphorbia milii. Des moul.

This research focused on evaluating the in vitro antioxidant, antidiabetic activities and Insilco docking properties of two phytocompounds 3,3',4',5,7-Pentahydroxyflavone (Quercetin) and Stigmasta-5,22-dien-3ß-ol (Stigma sterol). These compounds were isolated from the methanol extract of stem and ethanol extract of flower of the E. milii plant. The antioxidant properties of these phytocompounds were studied using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) method. A comparison was made between the antioxidant activity of quercetin, stigma sterol, and the standard drug ascorbic acid. The results indicated that quercetin showed significant antioxidant activity, suggesting its potential as an effective antioxidant. Additionally, in vitro antidiabetic activity was investigated through α-amylase and α-glucosidase inhibition assays. Screened compounds exhibited significant % of inhibition values against two carbohydrate hydrolysing enzymes, α-amylase and α-glucosidase compared to reference drug (acarbose). Furthermore, molecular docking studies were conducted to analyse the interaction of the compounds with α-d-glucose (PDB ID: 3A4A) and PTP1B I (PDB ID: 1T49) as target molecules. These docking studies provided further insights to support the biological findings of the compounds isolated from E. milii aerial parts.

The tricot approach: an agile framework for decentralized on-farm testing supported by citizen science. A retrospective.

Matching crop varieties to their target use context and user preferences is a challenge faced by many plant breeding programs serving smallholder agriculture. Numerous participatory approaches proposed by CGIAR and other research teams over the last four decades have attempted to capture farmers' priorities/preferences and crop variety field performance in representative growing environments through experimental trials with higher external validity. Yet none have overcome the challenges of scalability, data validity and reliability, and difficulties in capturing socio-economic and environmental heterogeneity. Building on the strengths of these attempts, we developed a new data-generation approach, called triadic comparison of technology options (tricot). Tricot is a decentralized experimental approach supported by crowdsourced citizen science. In this article, we review the development, validation, and evolution of the tricot approach, through our own research results and reviewing the literature in which tricot approaches have been successfully applied. The first results indicated that tricot-aggregated farmer-led assessments contained information with adequate validity and that reliability could be achieved with a large sample. Costs were lower than current participatory approaches. Scaling the tricot approach into a large on-farm testing network successfully registered specific climatic effects of crop variety performance in representative growing environments. Tricot's recent application in plant breeding networks in relation to decision-making has (i) advanced plant breeding lines recognizing socio-economic heterogeneity, and (ii) identified consumers' preferences and market demands, generating alternative breeding design priorities. We review lessons learned from tricot applications that have enabled a large scaling effort, which should lead to stronger decision-making in crop improvement and increased use of improved varieties in smallholder agriculture.

Autoimmune lymphoproliferative immunodeficiencies (ALPIDs): A proposed approach to redefining ALPS and other lymphoproliferative immune disorders.

Chronic nonmalignant lymphoproliferation and autoimmune cytopenia are relevant manifestations of immunohematologic diseases of childhood. Their diagnostic classification is challenging but important for therapy. Autoimmune lymphoproliferative syndrome (ALPS) is a genetically defined inborn error of immunity combining these manifestations, but it can explain only a small proportion of cases. Diagnostic categories such as ALPS-like disease, common variable immunodeficiency, or Evans syndrome have therefore been used. Advances in genetics and increasing availablity of targeted therapies call for more therapy-oriented disease classification. Moreover, recent discoveries in the (re)analysis of genetic conditions affecting FAS signaling ask for a more precise definition of ALPS. In this review, we propose the term autoimmune lymphoproliferative immunodeficiencies for a disease phenotype that is enriched for patients with genetic diseases for which targeted therapies are available. For patients without a current molecular diagnosis, this term defines a subgroup of immune dysregulatory disorders for further studies. Within the concept of autoimmune lymphoproliferative immunodeficiencies, we propose a revision of the ALPS classification, restricting use of this term to conditions with clear evidence of perturbation of FAS signaling and resulting specific biologic and clinical consequences. This proposed approach to redefining ALPS and other lymphoproliferative conditions provides a framework for disease classification and diagnosis that is relevant for the many specialists confronted with these diseases.

PI3Kδ Pathway Dysregulation and Unique Features of Its Inhibition by Leniolisib in Activated PI3Kδ Syndrome and Beyond.

The phosphoinositide 3-kinase (PI3K) pathway regulates diverse cellular processes, with finely tuned PI3Kδ activity being crucial for immune cell development and function. Genetic hyperactivation of PI3Kδ causes the inborn error of immunity activated phosphoinositide 3-kinase δ syndrome (APDS). Several PI3Kδ inhibitors have been investigated as treatment options for APDS, but only leniolisib has shown both efficacy and tolerability. In contrast, severe immune-mediated adverse events such as colitis, neutropenia, and hepatotoxicity have been observed with other PI3Kδ inhibitors, particularly those indicated for hematological malignancies. We propose that leniolisib is distinguished from other PI3Kδ inhibitors due to its structure, specific inhibitory properties selectively targeting the δ isoform without overinhibition of the δ or γ isoforms, and the precise match between APDS mechanism of disease and drug mechanism of action.