Iron-promoted zirconia-alumina supported Ni catalyst for highly efficient and cost-effective hydrogen production via dry reforming of methane.

Developing cost-effective and high-performance catalyst systems for dry reforming of methane (DRM) is crucial for producing hydrogen (H2) sustainably. Herein, we investigate using iron (Fe) as a promoter and major alumina support in Ni-based catalysts to improve their DRM performance. The addition of iron as a promotor was found to add reducible iron species along with reducible NiO species, enhance the basicity and induce the deposition of oxidizable carbon. By incorporating 1 wt.% Fe into a 5Ni/10ZrAl catalyst, a higher CO2 interaction and formation of reducible "NiO-species having strong interaction with support" was observed, which led to an ∼80% H2 yield in 420 min of Time on Stream (TOS). Further increasing the Fe content to 2wt% led to the formation of additional reducible iron oxide species and a noticeable rise in H2 yield up to 84%. Despite the severe weight loss on Fe-promoted catalysts, high H2 yield was maintained due to the proper balance between the rate of CH4 decomposition and the rate of carbon deposit diffusion. Finally, incorporating 3 wt.% Fe into the 5Ni/10ZrAl catalyst resulted in the highest CO2 interaction, wide presence of reducible NiO-species, minimum graphitic deposit and an 87% H2 yield. Our findings suggest that iron-promoted zirconia-alumina-supported Ni catalysts can be a cheap and excellent catalytic system for H2 production via DRM.

Cranioplasty using patient specific implants Polyether ether ketone versus ultra-high molecular weight polyethylene: A prospective study.

This prospective study aimed to compare ultra-high molecular weight polyethylene (UHMWPE) with polyetheretherketone (PEEK) in computer-designed patient-specific implants (PSIs) for cranial defect reconstruction, in terms of complications and aesthetic outcomes. Primary or secondary cranioplasty-eligible patients were included, while patients with active infection or hydrocephalus, or unfit for general anesthesia, were excluded from the study. All the implants were designed and fabricated by the same maxillofacial surgeon using CAD/CAM technology. UHMWPE PSIs were used in group 1 and PEEK PSIs in group 2. Technically, UHMWPE could be milled to a thinner margin thickness than PEEK, which resulted in better handling properties and a smoother end finish. All patients were evaluated over a period of 6 months in terms of overall complications or implant failure as the primary outcome, according to Clavien-Dindo (CVD) grading, and cosmetic satisfaction with the aesthetic results, using a Likert scale, as the secondary outcome. In total, 22 cranioplasty patients were included, with a mean age of 30.8 years (SD = 16.3). Across both groups, 17 patients (77.3%) did not develop postoperative complications. These occurred in three patients in group 1 (CVD grade I, II, and IIIb) (27.3%) and in two patients in group 2 (CVD grade II, IIIa, and IIIb) (18.2%), with no statistical difference (p = 0.6). None of the cases in both groups developed any clinical or radiographic signs of infection, or suffered implant failure. The mean satisfaction score was 4.8 in group 1 and 4.5 in group 2 (SD = 0.6). The difference in satisfaction scores between the two was not statistically significant (p = 0.23). Although UHMWPEE was comparable to PEEK in terms of overall complication rates and cosmesis after craniectomy, UHMWPEE as a material exhibited greater resiliency in technically challenging cases with large, complex/midline-crossing designs, previously fitted meshes, or single-stage resection-reconstruction, allowing better marginal adaptation.

Comorbidities, medications, depression, and physical performance measures associated with severe cognitive impairments in community-dwelling adults.

Cognitive impairment negatively impacts health, psychological, social, and economic domains. Cognitive impairment commonly affects physical functions in older adults, whereas these are deteriorated. However, the prevalence and associated factors of cognitive impairment among community-dwelling adults in Saudi Arabia have not been investigated yet. This study aimed to examine the prevalence of severe cognitive impairment and its associated factors in community-dwelling older adults in Saudi Arabia. This cross-sectional study involved adults aged ≥50 years. Demographic data and clinical data, including number of medications and body mass index (BMI), were collected. Cognitive impairment and depressive symptoms were measured using Arabic versions of the Montreal Cognitive Assessment (MoCA) and Patient Health Questionnaire-9 (PHQ-9), respectively. The participants were divided into severe cognitive impairment and mild cognitive impairment or normal cognitive function groups based on a score of <20 or ≥20, respectively, using the MoCA. Physical measures included dynamic gait index (DGI) scores, timed up-and-go (TUG), 5 times sit-to-stand (5XSST), functional reach test, and 6-minute walk test. A total of 206 participants (female: n = 96) were included. The prevalence of severe cognitive impairment in the community-dwelling older adults was 12.6%. The number of chronic conditions (odds ratio [OR]: 2.31, p<0.001), number of medications (OR: 1.36, p = 0.003), and depressive symptoms using PHQ-9 (OR: 1.11, p = 0.009) were significantly associated with severe cognitive impairment after adjustment for other covariates, including age, sex, and BMI. Based on the physical function measures, only the scores for DGI (OR: 0.86, p = 0.003), TUG (OR: 1.16, p = 0.035) and 5XSST (OR: 1.25, p <0.001) were significantly associated with severe cognitive impairment. This study revealed a high prevalence of severe cognitive impairment among community-dwelling adults in Saudi Arabia. Its major risk factors include depressive symptoms, number of chronic conditions and medications, and physical measures, including DGI, TUG, and 5XSST.

Automated gall bladder cancer detection using artificial gorilla troops optimizer with transfer learning on ultrasound images.

The gallbladder (GB) is a small pouch and a deep tissue placed under the liver. GB Cancer (GBC) is a deadly illness that is complex to discover in an initial phase. Initial diagnosis can significantly enhance the existence rate. Non-ionizing energy, low cost, and convenience make the US a general non-invasive analytical modality for patients with GB diseases. Automatic recognition of GBC from US imagery is a significant issue that has gained much attention from researchers. Recently, machine learning (ML) techniques dependent on convolutional neural network (CNN) architectures have prepared transformational growth in radiology and medical analysis for illnesses like lung, pancreatic, breast, and melanoma. Deep learning (DL) is a region of artificial intelligence (AI), a functional medical tomography model that can help in the initial analysis of GBC. This manuscript presents an Automated Gall Bladder Cancer Detection using an Artificial Gorilla Troops Optimizer with Transfer Learning (GBCD-AGTOTL) technique on Ultrasound Images. The GBCD-AGTOTL technique examines the US images for the presence of gall bladder cancer using the DL model. In the initial stage, the GBCD-AGTOTL technique preprocesses the US images using a median filtering (MF) approach. The GBCD-AGTOTL technique applies the Inception module for feature extraction, which learns the complex and intrinsic patterns in the pre-processed image. Besides, the AGTO algorithm-based hyperparameter tuning procedure takes place, which optimally picks the hyperparameter values of the Inception technique. Lastly, the bidirectional gated recurrent unit (BiGRU) model helps classify gall bladder cancer. A series of simulation analyses were performed to ensure the performance of the GBCD-AGTOTL technique on the GBC dataset. The experimental outcomes inferred the enhanced abilities of the GBCD-AGTOTL in detecting gall bladder cancer.

The Effect of Pan-Retinal Photocoagulation on Central Macular Thickness in a Sample of Iraqi Patients With Proliferative Diabetic Retinopathy.

OBJECTIVE: To evaluate the effect of pan-retinal photocoagulation (PRP) on central macular thickness (CMT) in a sample of Iraqi patients with proliferative diabetic retinopathy (PDR). METHODS: This prospective study was conducted at Ghazi Al-Hariri for Surgical Specialties Hospital, Baghdad, from March 2024 to May 2024. A total of 24 eyes from 18 treatment-naive PDR patients with no previous diabetic macular edema (DME) were enrolled. Each eye received PRP in two sessions, one week apart, using the Nidek GYC 500 laser system. CMT was measured at baseline and four weeks after the second PRP session using the Topcon DRI Triton Plus optical coherence tomography (OCT). Statistical analyses, including paired t-tests and Shapiro-Wilk tests for normality, were performed to evaluate changes in CMT. RESULTS: The mean CMT increased from 258.4 ± 30.7 microns at baseline to 269.9 ± 36.8 microns post PRP, with a mean increase of 11.5 ± 26.3 microns. This increase was statistically significant (p = 0.042). The Shapiro-Wilk test confirmed that the data were approximately normally distributed both before (W = 0.960, p = 0.445) and after (W = 0.931, p = 0.103) PRP treatment. CONCLUSION: PRP significantly increases CMT in PDR patients, although no additional treatment for macular edema was necessary. These findings align with previous studies, suggesting that PRP-induced macular thickening is a common outcome. Further research is recommended to explore long-term effects and potential mitigation strategies.

A Comparative Study of Ocular Surface Disease in Childhood Glaucoma.

PURPOSE: To evaluate ocular surface disease (OSD) in patients with childhood glaucoma (CG). METHODS: A multicenter, comparative, cross-sectional study of 41 eyes of 27 patients with CG and 42 eyes of 42 healthy age- and sex-matched controls was conducted between 2021 and 2022. Developmentally delayed children and those having a coexisting systemic illness that might affect the ocular surface were not enrolled. Data collected included glaucoma type, glaucoma medications and surgeries, anterior segment examination, symptom frequency and severity, and tear break-up time (TBUT). Dry eye disease (DED) symptoms were evaluated using an Arabic-language SPEED questionnaire. RESULTS: The average number of glaucoma medications and surgeries among the 27 patients with CG was 1.17 and 1.4, respectively. TBUT was shorter in patients with CG compared with controls and even shorter when glaucoma medication usage was accounted for. Punctate epithelial erosions were more prevalent in patients with CG and of a higher grade. The severity of all DED parameters, and the prevalence of all DED parameters except for eye fatigue, were greater in patients with CG on the day of questionnaire intake. In addition, each additional glaucoma drop was associated with worse TBUT and ocular surface dryness. All analyses mentioned above were statistically significant (P < 0.05). CONCLUSIONS: In this first study to comprehensively evaluate OSD in patients with CG, a significantly greater incidence of OSD signs and symptoms was found in patients with CG compared with a healthy population. Additional study by CG subtype is needed.

Cost-effectiveness of ace inhibitors versus ARBs in heart failure management.

BACKGROUND: Heart failure is a chronic condition that imposes a significant burden on healthcare systems worldwide. Effective management is crucial for improving patient outcomes and reducing costs. Angiotensin-converting enzyme (ACE) inhibitors and angiotensin II receptor blockers (ARBs) are widely used to manage heart failure by reducing cardiac strain and preventing disease progression. Despite their common use, ACE inhibitors and ARBs differ in mechanisms, cost, and potential side effects. ACE inhibitors have long been the standard treatment, while ARBs are often prescribed to patients intolerant to ACE inhibitors, particularly due to side effects like cough. Given these differences, evaluating the cost-effectiveness of these treatments is essential. This study compares the cost-effectiveness of ACE inhibitors and ARBs from a healthcare system perspective, considering both direct medical costs and health outcomes. METHODS: A cost-effectiveness analysis was conducted using a decision-analytic Markov model to simulate heart failure progression in a hypothetical cohort. Data inputs included clinical trial outcomes, real-world effectiveness data, direct medical costs (medications, hospitalizations, monitoring), and utility values for quality of life. The primary outcome measures were the cost per quality-adjusted life year gained and the incremental cost-effectiveness ratio. Sensitivity analyses tested the robustness of results, and subgroup analyses were conducted based on age and disease severity. RESULTS: The base-case analysis showed that ACE inhibitors were associated with lower overall costs and slightly higher quality-adjusted life years than ARBs. Sensitivity analyses revealed that variations in key parameters, such as transition probabilities, mortality rates, and healthcare expenses, had limited impact on the overall cost-effectiveness conclusions. Subgroup analyses indicated that ACE inhibitors and ARBs exhibited similar cost-effectiveness profiles for patients aged <65 and ≥65 years. However, among patients with severe heart failure, ARBs demonstrated a higher incremental cost-effectiveness ratio compared with ACE inhibitors, suggesting reduced cost-effectiveness in this subgroup. CONCLUSION: ACE inhibitors are likely a more cost-effective option for managing heart failure than ARBs, particularly from a healthcare system perspective. The findings underscore the importance of tailoring treatment decisions to individual patient factors, preferences, and clinical conditions, providing valuable insights for healthcare policy and practice, particularly regarding cost-effectiveness across patient subgroups.

Highly sensitive detection of kojic acid in food samples using fluorescent carbon dots derived from pomegranate peel.

Kojic acid (KA) has gained significant attention due to its widespread use in the food and cosmetics industries. However, concerns about its potential carcinogenic effects have heightened the need for sensitive detection methods. This study introduces a fluorescence-based optical sensor for the quantification of KA in food samples, utilizing fluorescent carbon dots (CDs) synthesized from pomegranate peel via a hydrothermal method. The Stern-Volmer plot demonstrated a linear response for KA in the range of 120 to 1200 µM, with a Pearson correlation coefficient (r) of 0.9999 and. The sensor exhibited a detection limit of 30 ± 0.04 µM and a limit of quantification (LOQ) of 90 ± 0.14 µM. Application of the developed method to soy sauce and vinegar samples yielded accurate KA determinations, with recoveries of 103.11 ± 0.96% and 104.45 ± 2.15%, respectively. These findings highlight the potential of the proposed sensor for practical applications in food quality and safety assessment, offering valuable insights into the presence of KA in food products.

Parameter extraction of proton exchange membrane fuel cell based on artificial rabbits' optimization algorithm and conducting laboratory tests.

Proton exchange membrane fuel cell (PEMFC) parameter extraction is an important issue in modeling and control of renewable energies. The PEMFC problem's main objective is to estimate the optimal value of unknown parameters of the electrochemical model. The main objective function of the optimization problem is the sum of the square errors between the measured voltages and output voltages of the proposed electrochemical optimized model at various loading conditions. Natural rabbit survival strategies such as detour foraging and random hiding are influenced by Artificial rabbit optimization (ARO). Meanwhile, rabbit energy shrink is mimicked to control the smooth switching from detour foraging to random hiding. In this work, the ARO algorithm is proposed to find the parameters of PEMFC. The ARO performance is verified using experimental results obtained from conducting laboratory tests on the fuel cell test system (SCRIBNER 850e, LLC). The simulation results are assessed with four competitive algorithms: Grey Wolf Optimization Algorithm, Particle Swarm Optimizer, Salp Swarm Algorithm, and Sine Cosine Algorithm. The comparison aims to prove the superior performance of the proposed ARO compared with the other well-known competitive algorithms.

Development and validation of a short version of the quality of life-DSD questionnaire for parents of young children with conditions affecting sex development.

BACKGROUND: There is a paucity of information on health-related quality of life (HRQoL) outcomes in parents and children with conditions affecting sex development. The objective of this study was to develop short forms of HRQoL questionnaires which consist of a 63-item and 25-item parent self-report (PSR) and parent proxy-report (PPR), respectively, optimising use in routine clinic settings. METHODS: Short questionnaires were developed following exploratory factor analysis using raw data from 132 parents. Long and short PSRs were completed by 24 parents of children with conditions affecting sex development with median age of 3.6 years (range 0.1, 6.6); 21 (88%) were boys and 11 (46%) had proximal hypospadias. A subset of 19 parents completed long and short PPRs. RESULTS: Item selection based on factor loadings of >0.8 and expert consultation, produced short PSR and PPRs containing 16 and 7 items, respectively. There was no statistically significant difference in 11 out of 12 (92%) scales on the PSR and 4 out of 5 (80%) scales on the PPR when comparing short and long questionnaire scores. Short and long questionnaires took <1 minute and 5 minutes for completion, respectively. Eighteen parents (75%) reported that the time taken to complete short questionnaires was acceptable; 10 (42%) preferred short questionnaires. Ten (42%) versus 6 (25%) stated a preference for completing short versus long questionnaires. CONCLUSION: Short versions were largely representative of the long questionnaires and are acceptable to evaluate psychosocial distress in young children and their caregivers. Further psychometric validation of short forms is warranted.

Deep Learning-Based Image Classification and Segmentation on Digital Histopathology for Oral Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.

BACKGROUND: Artificial intelligence (AI)-based tools have shown promise in histopathology image analysis in improving the accuracy of oral squamous cell carcinoma (OSCC) detection with intent to reduce human error. OBJECTIVES: This systematic review and meta-analysis evaluated deep learning (DL) models for OSCC detection on histopathology images by assessing common diagnostic performance evaluation metrics for AI-based medical image analysis studies. METHODS: Diagnostic accuracy studies that used DL models for the analysis of histopathological images of OSCC compared to the reference standard were analyzed. Six databases (PubMed, Google Scholar, Scopus, Embase, ArXiv, and IEEE) were screened for publications without any time limitation. The QUADAS-2 tool was utilized to assess quality. The meta-analyses included only studies that reported true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) in their test sets. RESULTS: Of 1267 screened studies, 17 studies met the final inclusion criteria. DL methods such as image classification (n = 11) and segmentation (n = 3) were used, and some studies used combined methods (n = 3). On QUADAS-2 assessment, only three studies had a low risk of bias across all applicability domains. For segmentation studies, 0.97 was reported for accuracy, 0.97 for sensitivity, 0.98 for specificity, and 0.92 for Dice. For classification studies, accuracy was reported as 0.99, sensitivity 0.99, specificity 1.0, Dice 0.95, F1 score 0.98, and AUC 0.99. Meta-analysis showed pooled estimates of 0.98 sensitivity and 0.93 specificity. CONCLUSION: Application of AI-based classification and segmentation methods on image analysis represents a fundamental shift in digital pathology. DL approaches demonstrated significantly high accuracy for OSCC detection on histopathology, comparable to that of human experts in some studies. Although AI-based models cannot replace a well-trained pathologist, they can assist through improving the objectivity and repeatability of the diagnosis while reducing variability and human error as a consequence of pathologist burnout.

Ferroelastic ionic organic crystals that self-heal to 95.

The realm of self-healing materials integrates chemical and physical mechanisms that prevent wear and fracturing and extend the operational lifetime. Unlike the favorable rheology of amorphous soft materials that facilitates efficient contact between fragments, the efficiency of recovery of atomistically ordered materials is restricted by slower interfacial mass transport and the need for ideal physical alignment, which limits their real-world application. We report drastic enhancements in efficiency and recovery time in the self-healing of anilinium bromide, challenging these limitations. Crystals of this material recovered up to 49% within seconds and up to 95% after 100 min via ferroelastic detwinning. The spatial evolution of strain during cracking and healing was measured in real time using digital image correlation. Favorable alignment and strong ionic bonding across the interface of partially fractured crystals facilitate self-healing. This study elevates organic crystals close to the best-in-class self-healing polymers and sets an approach for durable crystal-based optoelectronics.

Screening for orthosomnia: a reliability generalization meta-analysis of the Anxiety and Preoccupation about Sleep Questionnaire (APSQ).

This mini-meta-analysis evaluated the internal consistency of the Anxiety and Preoccupation about Sleep Questionnaire (APSQ) across existing studies to assess its potential as an orthosomnia (an obsessive preoccupation with achieving perfect sleep) screening tool. A systematic literature search identified four studies with 2,506 participants using English, Swedish, Turkish, and Arabic versions. Cronbach's alpha ranged from 0.91 to 0.95 across studies. The APSQ demonstrated high overall internal consistency reliability (pooled Cronbach's alpha of the entire ASPQ = 0.93, 95% CI 0.91-0.94), suggesting utility for screening orthosomnia symptoms. The pooled Cronbach's alpha of the first and second factors of the ASPQ were: 0.91 (95% CI 0.89-0.93) and 0.87 (95% CI 0.84-0.89), respectively. APSQ demonstrated high overall internal consistency reliability; however, limited linguistic/cultural representation and significant heterogeneity across studies impact generalizability.

A global dataset of gross nitrogen transformation rates across terrestrial ecosystems.

Rates of nitrogen transformations support quantitative descriptions and predictive understanding of the complex nitrogen cycle, but measuring these rates is expensive and not readily available to researchers. Here, we compiled a dataset of gross nitrogen transformation rates (GNTR) of mineralization, nitrification, ammonium immobilization, nitrate immobilization, and dissimilatory nitrate reduction to ammonium in terrestrial ecosystems. Data were extracted from 331 studies published from 1984-2022, covering 581 sites. Globally, 1552 observations were appended with standardized soil, vegetation, and climate data (49 variables in total) potentially contributing to the observed variations of GNTR. We used machine learning-based data imputation to fill in partially missing GNTR, which improved statistical relationships between theoretically correlated processes. The dataset is currently the most comprehensive overview of terrestrial ecosystem GNTR and serves as a global synthesis of the extent and variability of GNTR across a wide range of environmental conditions. Future research can utilize the dataset to identify measurement gaps with respect to climate, soil, and ecosystem types, delineate GNTR for certain ecoregions, and help validate process-based models.

Tapping into haloalkaliphilic bacteria for sustainable agriculture in treated wastewater: insights into genomic fitness and environmental adaptation.

The increasing salinity and alkalinity of soils pose a global challenge, particularly in arid regions such as Tunisia, where about 50% of lands are sensitive to soil salinization. Anthropogenic activities, including the use of treated wastewater (TWW) for irrigation, exacerbate these issues. Haloalkaliphilic bacteria, adapted to TWW conditions and exhibiting plant-growth promotion (PGP) and biocontrol traits, could offer solutions. In this study, 24 haloalkaliphilic bacterial strains were isolated from rhizosphere sample of olive tree irrigated with TWW for more than 20 years. The bacterial identification using 16S rRNA gene sequencing showed that the haloalkaliphilic isolates, capable of thriving in high salinity and alkaline pH, were primarily affiliated to Bacillota (Oceanobacillus and Staphylococcus). Notably, these strains exhibited biofertilization and enzyme production under both normal and saline conditions. Traits such as phosphate solubilization, and the production of exopolysaccharide, siderophore, ammonia, and hydrogen cyanide were observed. The strains also demonstrated enzymatic activities, including protease, amylase, and esterase. Four selected haloalkaliphilic PGPR strains displayed antifungal activity against Alternaria terricola, with three showing tolerances to heavy metals and pesticides. The strain Oceanobacillus picturea M4W.A2 was selected for genome sequencing. Phylogenomic analyses indicated that the extreme environmental conditions probably influenced the development of specific adaptations in M4W.A2 strain, differentiating it from other Oceanobacillus picturae strains. The presence of the key genes associated with plant growth promotion, osmotic and oxidative stress tolerance, antibiotic and heavy metals resistance hinted the functional capabilities might help the strain M4W.A2 to thrive in TWW-irrigated soils. By demonstrating this connection, we aim to improve our understanding of genomic fitness to stressed environments. Moreover, the identification of gene duplication and horizontal gene transfer events through mobile genetic elements allow the comprehension of these adaptation dynamics. This study reveals that haloalkaliphilc bacteria from TWW-irrigated rhizosphere exhibit plant-growth promotion and biocontrol traits, with genomic adaptations enabling their survival in high salinity and alkaline conditions, offering potential solutions for soil salinization issues.

Chemical Characterization, Evaluation of Acute Oral Toxicity, and Anti-Diabetic Activity of Aloe sabaea Flowers Extract on Alloxan-Induced Diabetic Rats.

The study aimed to conduct chemical profiling, acute in-vivo toxicity evaluation, and the potential anti-diabetic effect of standardized Aloe sabaea flowers ethanolic extracts (ASFEE) on alloxan-induced diabetic rats. The chemical composition was analyzed using GC-MS and TLC techniques. The oral acute toxicity study was performed according to the WHO 2000 and the OECD 420 guidelines. Furthermore, anti-diabetic activity was investigated using two doses of ASFEE (0.2 and 0.5 g/kg/day BW, p.o.) compared with glibenclamide (5 mg/kg/day, p.o.). A molecular docking investigation of the identified components with the PTPN9 enzyme was performed to figure out the proposed anti-diabetic mechanism. GC-MS analysis displayed the existence of 18 compounds; most of the compounds were fatty acids and their esters, and phytosterols. Total phenolic and flavonoid contents were 42.00±1.26 mg GAE/g DW and 22.21±1.55 mg QE/g DW, respectively. The results of the in-vivo toxicity study revealed the absence of noticeable signs of toxicity or mortality at various doses establishing the safety of the tested extract. The estimated LD50 value was higher than 10 g/kg. Antidiabetic action exhibited a noticeable decline in fasting blood glucose (FBG) levels comparable to glibenclamide with no inducing intense hypoglycemia and considerable excess weight.

Temporal Trends in Acute Adrenal Insufficiency Events in Children With Congenital Adrenal Hyperplasia During 2019-2022.

Background: It is unclear whether targeted monitoring of acute adrenal insufficiency (AI) related adverse events (AE) such as sick day episodes (SDEs) and hospitalization rate in congenital adrenal hyperplasia (CAH) is associated with a change in the occurrence of these events. Aim: Study temporal trends of AI related AE in the I-CAH Registry. Methods: In 2022, data on the occurrence of AI-related AE in children aged <18 years with 21-hydroxylase deficiency CAH were compared to data collected in 2019. Results: In 2022, a total of 513 children from 38 centers in 21 countries with a median of 8 children (range 1-58) per center had 2470 visits evaluated over a 3-year period (2019-2022). The median SDE per patient year in 2022 was 0 (0-2.5) compared to 0.3 (0-6) in 2019 (P = .01). Despite adjustment for age, CAH phenotype and duration of study period, a difference in SDE rate was still apparent between the 2 cohorts. Of the 38 centers in the 2022 cohort, 21 had also participated in 2019 and a reduction in SDE rate was noted in 13 (62%), an increase was noted in 3 (14%), and in 5 (24%) the rate remained the same. Of the 474 SDEs reported in the 2022 cohort, 103 (22%) led to hospitalization compared to 299 of 1099 SDEs (27%) in the 2019 cohort (P = .02). Conclusion: The I-CAH Registry can be used for targeted monitoring of important clinical benchmarks in CAH. However, changes in reported benchmarks need careful interpretation and longer-term monitoring.

LncRNAs orchestration of gastric cancer - particular emphasis on the etiology, diagnosis, and treatment resistance.

Gastric cancer (GC) remains a major public health challenge worldwide. Long non-coding RNAs (lncRNAs) play important roles in the development, progression, and resistance to the treatment of GC, as shown by recent developments in molecular characterization. Still, an in-depth investigation of the lncRNA landscape in GC is absent. However, The objective of this systematic review is to evaluate our present understanding of the role that lncRNA dysregulation plays in the etiology of GC and treatment resistance, with a focus on the underlying mechanisms and clinical implications. Research that described the functions of lncRNA in angiogenesis, stemness, epigenetics, metastasis, apoptosis, development, and resistance to key treatments was given priority. In GC, it has been discovered that a large number of lncRNAs, including MALAT1, HOTAIR, H19, and ANRIL, are aberrantly expressed and are connected with disease-related outcomes. Through various methods such as chromatin remodeling, signal transduction pathways, and microRNA sponging, they modulate hallmark cancer capabilities. Through the activation of stemness programs, epithelial-mesenchymal transition (EMT), and survival signaling, LncRNAs also control resistance to immunotherapy, chemotherapy, and targeted therapies. By clarifying their molecular roles further, we may be able to identify new treatment targets and ways to overcome resistance. This article aims to explore the interplay between lncRNAs, and GC. Specifically, the focus is on understanding how lncRNAs contribute to the etiology of GC and influence treatment resistance in patients with this disease.