Polymer-based antibody mimetics (iBodies) target human PD-L1 and function as a potent immune checkpoint blocker.

Immune checkpoint blockade (ICB) using monoclonal antibodies against programmed cell death protein 1 (PD-1) or programmed death-ligand 1 (PD-L1) is the treatment of choice for cancer immunotherapy. However, low tissue permeability, immunogenicity, immune-related adverse effects, and high cost could be possibly improved using alternative approaches. On the other hand, synthetic low-molecular-weight (LMW) PD-1/PD-L1 blockers have failed to progress beyond in vitro studies, mostly due to low binding affinity or poor pharmacological characteristics resulting from their limited solubility and/or stability. Here, we report the development of polymer-based anti-human PD-L1 antibody mimetics (α-hPD-L1 iBodies) by attaching the macrocyclic peptide WL12 to a N-(2-hydroxypropyl)methacrylamide copolymer. We characterized the binding properties of iBodies using surface plasmon resonance, enzyme-linked immunosorbent assay, flow cytometry, confocal microscopy, and a cellular ICB model. We found that the α-hPD-L1 iBodies specifically target human PD-L1 (hPD-L1) and block the PD-1/PD-L1 interaction in vitro, comparable to the atezolizumab, durvalumab, and avelumab licensed monoclonal antibodies targeting PD-L1. Our findings suggest that iBodies can be used as experimental tools to target hPD-L1 and could serve as a platform to potentiate the therapeutic effect of hPD-L1-targeting small molecules by improving their affinity and pharmacokinetic properties.

Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features.

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir. Moreover, to optimize the outcomes of the developed hybrid model, we considered the impact of a decomposition technique, specifically the wavelet transform (WT). In addition to these efforts, we created two distinct machine learning (ML) algorithms-namely, random forest (RF) and support vector regression (SVR)-to demonstrate the superior performance of deep learning algorithms over individual ML ones. We initially gathered WQIs from diverse locations and varying depths within the reservoir using an AAQ-RINKO device in the study area to achieve this. It is important to highlight that, despite utilizing diverse data-driven models in water quality estimation, a significant gap persists in the existing literature regarding implementing a comprehensive hybrid algorithm. This algorithm integrates the wavelet transform, convolutional neural network (CNN), and gated recurrent unit (GRU) methodologies to estimate WQIs accurately within a spatiotemporal framework. Subsequently, the effectiveness of the models that were developed was assessed utilizing various statistical metrics, encompassing the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) throughout both the training and testing phases. The findings demonstrated that the WT-CNN-GRU model exhibited better performance in comparison with the other algorithms by 13% (SVR), 13% (RF), 9% (CNN), and 8% (GRU) when R-squared and DO were considered as evaluation indices and WQIs, respectively.

Methylation Status of miR-34a and miR-126 in Non-Small Cell Lung Cancer (NSCLC) Tumor Tissues.

Background: MiR-34a and miR-126 mainly act as tumor suppressors and are often downregulated in various cancers, including non-small cell lung cancer (NSCLC). We aimed to determine the methylation status of miR-34a and miR-126 in NSCLC patients. Methods: The current study included 63 paraffin-embedded NSCLC and paired adjacent normal tissues. After DNA extraction and bisulfite treatment, the methylation status of miR-34a and miR-126 were evaluated using the MSP method. Results: There was no statistically significant difference between tumor and normal tissues regarding the methylation status of miR-34a and miR-126 (p > 0.05). Moreover, we found no significant correlation between the methylation status of miR-34a and miR-126 with patients' demographic parameters, including gender, age, and pathology subtype (p > 0.05). Conclusion: Considering the low expression of mir-126 and mir-34 in NSCLC, more sensitive methods are recommended to be exploited for detecting the level of methylation or underlying mechanisms other than promoter hypermethylation in silencing these genes in NSCLC.

Meta-analysis: Risk of pancreatic cancer in patients with inflammatory bowel disease.

BACKGROUND: Studies exploring the association between inflammatory bowel disease (IBD) and pancreatic cancer have reported inconsistent results. AIMS: To provide a comprehensive overview of the risk of pancreatic cancer development in patients with IBD. METHODS: We searched Embase, PubMed, Scopus and ProQuest from inception to 31 October 2023. We included population-based cohort studies examining the risk of incident pancreatic cancer in adult patients with IBD compared to the non-IBD population. We also retrieved Mendelian randomisation (MR) studies investigating the relationship of IBD with pancreatic cancer risk. We conducted random-effects meta-analyses and provided pooled relative risks (RRs) with 95% confidence intervals (CIs). RESULTS: We included 13 studies. Among 11 cohort studies, the risk of developing pancreatic cancer increased by 79% in patients with IBD (RR = 1.79 [95% CI: 1.16-2.75]; I2 = 95.7%). Patients either with Crohn's disease (RR = 1.42 [95% CI: 1.24-1.63]) or ulcerative colitis (RR = 1.50 [95% CI: 1.17-1.92]) had increased risk (p for interaction = 0.72). The annual incidence of pancreatic cancer potentially attributable to IBD increased by 55 cases (95% CI: 17-103) per million. Two MR studies demonstrated that genetic liability to IBD was associated with an increased risk of pancreatic cancer. CONCLUSIONS: Our results suggest a moderate increase in the risk of pancreatic cancer in patients with IBD, which may be further heightened by genetic predisposition to IBD. The increased risk of pancreatic cancer is probably similar in Crohn's disease and ulcerative colitis.

Tuberculosis and lung cancer: metabolic pathways play a key role.

Despite the fact that some cases of tuberculosis (TB) are undiagnosed and untreated, it remains a serious global public health issue. In the diagnosis, treatment, and control of latent and active TB, there may be a lack of effectiveness. An understanding of metabolic pathways can be fundamental to treat latent TB infection and active TB disease. Rather than targeting Mycobacterium tuberculosis, the control strategies aim to strengthen host responses to infection and reduce chronic inflammation by effectively enhancing host resistance to infection. The pathogenesis and progression of TB are linked to several metabolites and metabolic pathways, and they are potential targets for host-directed therapies. Additionally, metabolic pathways can contribute to the progression of lung cancer in patients with latent or active TB. A comprehensive metabolic pathway analysis is conducted to highlight lung cancer development in latent and active TB. The current study aimed to emphasize the association between metabolic pathways of tumor development in patients with latent and active TB. Health control programs around the world are compromised by TB and lung cancer due to their special epidemiological and clinical characteristics. Therefore, presenting the importance of lung cancer progression through metabolic pathways occurring upon TB infection can open new doors to improving control of TB infection and active TB disease while stressing that further evaluations are required to uncover this correlation.

Risk of Common Neurological Disorders in Adult Patients with Inflammatory Bowel Disease: A Systematic Review and Meta-analysis.

BACKGROUND: Several studies investigated the risks of neurological conditions in patients with inflammatory bowel disease (IBD), with some variability in findings. We aimed to perform a systematic review and meta-analysis of available evidence to elucidate the association between IBD and the risks of common neurological disorders. METHODS: We conducted a literature search through Embase, PubMed, Scopus, and ProQuest databases from inception to June 30, 2023, to identify cohort studies assessing the risk of developing stroke, all-cause dementia, Parkinson's disease (PD), multiple sclerosis (MS), seizure/epilepsy, and peripheral neuropathy in adult IBD patients compared with non-IBD population. We combined hazard ratios (HRs) with 95% confidence intervals (CIs) to compute pooled estimates using a random-effects model. RESULTS: In total, 22 cohort studies were included, of which 9 studies reported 7074 stroke events in 202 460 IBD patients, 5 studies reported 3783 all-cause dementia diagnoses in 109 602 IBD patients, 7 studies reported 932 PD diagnoses in 354 792 IBD patients, and 1 study reported 6 MS events in 35 581 IBD patients. We observed increased risks of incident stroke (pooled HR = 1.19; 95% CI, 1.06-1.31), all-cause dementia (pooled HR = 1.22; 95% CI, 1.05-1.38), PD (pooled HR = 1.39; 95% CI, 1.20-1.58), and MS (HR = 2.89; 95% CI, 1.02-8.42). No eligible studies were found on peripheral neuropathy and seizure/epilepsy. CONCLUSIONS: Inflammatory bowel disease may be modestly associated with increased risks of stroke, all-cause dementia, and PD. Further longitudinal studies are warranted to investigate potential links with MS, seizure/epilepsy, and peripheral neuropathy, as well as their clinical significance.

This systematic review and meta-analysis of cohort studies aimed to clarify association between inflammatory bowel disease and risks of common neurological disorders. Based on analyses, inflammatory bowel disease may modestly increase risks of stroke, all-cause dementia, and Parkinson's disease vs the healthy population.

The effects of green tea extract supplementation on body composition, obesity-related hormones and oxidative stress markers: a grade-assessed systematic review and dose-response meta-analysis of randomised controlled trials.

Research indicates that green tea extract (GTE) supplementation is beneficial for a range of conditions, including several forms of cancer, CVD and liver diseases; nevertheless, the existing evidence addressing its effects on body composition, oxidative stress and obesity-related hormones is inconclusive. This systematic review and meta-analysis aimed to investigate the effects of GTE supplementation on body composition (body mass (BM), body fat percentage (BFP), fat mass (FM), BMI, waist circumference (WC)), obesity-related hormones (leptin, adiponectin and ghrelin) and oxidative stress (malondialdehyde (MDA) and total antioxidant capacity (TAC)) markers. We searched proper databases, including PubMed/Medline, Scopus and Web of Science, up to July 2022 to recognise published randomised controlled trials (RCT) that investigated the effects of GTE supplementation on the markers mentioned above. A random effects model was used to carry out a meta-analysis. The heterogeneity among the studies was assessed using the I2 index. Among the initial 11 286 studies identified from an electronic database search, fifty-nine studies involving 3802 participants were eligible to be included in this meta-analysis. Pooled effect sizes indicated that BM, BFP, BMI and MDA significantly reduced following GTE supplementation. In addition, GTE supplementation increased adiponectin and TAC, with no effects on FM, leptin and ghrelin. Certainty of evidence across outcomes ranged from low to high. Our results suggest that GTE supplementation can attenuate oxidative stress, BM, BMI and BFP, which are thought to negatively affect human health. Moreover, GTE as a nutraceutical dietary supplement can increase TAC and adiponectin.

The effects of conjugated linoleic acid supplementation on anthropometrics and body composition indices in adults: a systematic review and dose-response meta-analysis.

Prior meta-analytic investigations over a decade ago rather inconclusively indicated that conjugated linoleic acid (CLA) supplementation could improve anthropometric and body composition indices in the general adult population. More recent investigations have emerged, and an up-to-date systematic review and meta-analysis on this topic must be improved. Therefore, this investigation provides a comprehensive systematic review and meta-analysis of randomised controlled trials (RCT) on the impact of CLA supplementation on anthropometric and body composition (body mass (BM), BMI, waist circumference (WC), fat mass (FM), body fat percentage (BFP) and fat-free mass (FFM)) markers in adults. Online databases search, including PubMed, Scopus, the Cochrane Library and Web of Science up to March 2022, were utilised to retrieve RCT examining the effect of CLA supplementation on anthropometric and body composition markers in adults. Meta-analysis was carried out using a random-effects model. The I2 index was used as an index of statistical heterogeneity of RCT. Among the initial 8351 studies identified from electronic databases search, seventy RCT with ninety-six effect sizes involving 4159 participants were included for data analyses. The results of random-effects modelling demonstrated that CLA supplementation significantly reduced BM (weighted mean difference (WMD): -0·35, 95 % CI (-0·54, -0·15), P < 0·001), BMI (WMD: -0·15, 95 % CI (-0·24, -0·06), P = 0·001), WC (WMD: -0·62, 95% CI (-1·04, -0·20), P = 0·004), FM (WMD: -0·44, 95 % CI (-0·66, -0·23), P < 0·001), BFP (WMD: -0·77 %, 95 % CI (-1·09, -0·45), P < 0·001) and increased FFM (WMD: 0·27, 95 % CI (0·09, 0·45), P = 0·003). The high-quality subgroup showed that CLA supplementation fails to change FM and BFP. However, according to high-quality studies, CLA intake resulted in small but significant increases in FFM and decreases in BM and BMI. This meta-analysis study suggests that CLA supplementation may result in a small but significant improvement in anthropometric and body composition markers in an adult population. However, data from high-quality studies failed to show CLA's body fat-lowering properties. Moreover, it should be noted that the weight-loss properties of CLA were small and may not reach clinical importance.

Performance of artificial intelligence using cone-beam computed tomography for segmentation of oral and maxillofacial structures: A systematic review and meta-analysis.

Background: There are different values reported about the performance of artificial intelligence using cone-beam computed tomography (CBCT) for segmentation of oral and maxillofacial structures. We aimed to perform a systematic review and meta-analysis to provide an overall estimate to resolve the given conflicts. Material and Methods: A literature search was conducted in Embase, PubMed, and Scopus through 31 October 2022, to identify studies evaluating artificial intelligence systems using oral and maxillofacial CBCT images for automatic segmentation of anatomical landmarks. The surveys had to report the outcome according to dice coefficient (DICE) or dice similarity coefficient (DSC) index. The estimates were presented as percent and 95% confidence interval (CI). I-squared index was used to assess the heterogeneity between the surveys. Results: A total of 24 eligible studies were finally enrolled. The overall pooled DICE/DSC value for artificial intelligence was 0.92 (95% CI: 0.88-0.95; I-squared=93.6%, p<0.001). Tooth and mandible were evaluated more than other anatomical regions (five studies for each one). The lowest and highest DICE/DSC scores for the artificial intelligence related to inferior alveolar nerve (0.55 [95% CI: 0.47-0.63]) and pharyngeal airway and sinonasal cavity (0.98 [95% CI: 0.98-1.00]). Conclusions: The findings revealed excellent performance for the artificial intelligence regarding the segmentation task of oral and maxillofacial CBCT images. Key words:Artificial intelligence, cone-beam computed tomography, segmentation performance, dentistry.

Comparison of the Effectiveness of Dexmedetomidine-Ketamine and Midazolam-Ketamine Regimens in Sedation of Children Treated with Extracorporeal Shock Wave Lithotripsy.

Background: Despite the high acceptability of the extracorporeal shock wave lithotripsy (ESWL) procedure in the treatment of urinary stones at all ages, it is necessary to use a variety of analgesic drugs during the procedure, especially among children. Objectives: We aimed to evaluate the effect of dexmedetomidine-ketamine (DK) and midazolam-ketamine (MK) compounds in the sedation of children (2-6 years old) undergoing ESWL. Methods: This randomized, double-blind clinical trial was performed on children aged 2 to 6 years with renal stones undergoing ESWL. The participants were randomly assigned to the DK and MK regimen groups (dexmedetomidine, 0.05 mcg/kg within 10 minutes infusion; midazolam, 0.05 mg/kg within 3 minutes infusion; ketamine, 0.5 mg/kg bolus injection). The patients were assessed with respect to sedation degree, post-procedure hemodynamic status, recovery time and awakening, and operator satisfaction. Results: Recovery time was significantly shorter in the DK group than in the MK group. Also, the DK regimen was more analgesic than the MK regimen; therefore, the need to repeat ketamine administration was less. There was no difference between the 2 methods in terms of cooperation at the time of separation of children from their parents, patient cooperation during the procedure, average verbal response time and average cooperation time after entering recovery, and operator satisfaction with the operation. No side effects were observed in the 2 groups. Conclusions: Ketamine with dexmedetomidine is associated with greater analgesia and shorter recovery time; however, sedation time was longer (insignificant) in ketamine with midazolam than in ketamine with dexmedetomidine. Thus, ketamine with dexmedetomidine is more preferred.

Optimal waste load allocation in river systems based on a new multi-objective cuckoo optimization algorithm.

Water pollution escalates with rising waste discharge in river systems, as the rivers' limited pollution tolerance and constrained self-cleaning capacity compel the release of treated pollutants. Although several studies have shown that the non-dominated sorting genetic algorithm-II (NSGA-II) is an effective algorithm regarding the management of river water quality to reach water quality standards, to our knowledge, the literature lacks using a new optimization model, namely, the multi-objective cuckoo optimization algorithm (MOCOA). Therefore, this research introduces a new optimization framework, including non-dominated sorting and ranking selection using the comparison operator densely populated towards the best Pareto front and a trade-off estimation between the goals of discharges and environmental protection authorities. The suggested algorithm is implemented for a waste load allocation issue in Jajrood River, located in the North of Iran. The limitation of this research is that discharges are point sources. To analyze the performance of the new optimization algorithm, the simulation model is linked with a hybrid optimization model using a cuckoo optimization algorithm and non-dominated sorting genetic algorithms to convert a single-objective algorithm to a multi-objective algorithm. The findings indicate that, in terms of violation index and inequity values, MOCOA's Pareto front is superior to NSGA-II, which highlights the MOCOA's effectiveness in waste load allocation. For instance, with identical population sizes and violation indexes for both algorithms, the optimal Pareto front ranges from 1.31 to 2.36 for NSGA-II and 0.379 to 2.28 for MOCOA. This suggests that MOCOA achieves a superior Pareto front in a more efficient timeframe. Additionally, MOCOA can attain optimal equity in the smaller population size.

Forecasting water quality variable using deep learning and weighted averaging ensemble models.

Water quality variables, including chlorophyll-a (Chl-a), play a pivotal role in comprehending and evaluating the condition of aquatic ecosystems. Chl-a, a pigment present in diverse aquatic organisms, notably algae and cyanobacteria, serves as a valuable indicator of water quality. Thus, the objectives of this study encompass: (1) the assessment of the predictive capabilities of four deep learning (DL) models - namely, recurrent neural network (RNN), long short-term memory (LSTM), gated recurrence unit (GRU), and temporal convolutional network (TCN) - in forecasting Chl-a concentrations; (2) the incorporation of these DL models into ensemble models (EMs) employing genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II) to harness the strengths of each standalone model; and (3) the evaluation of the efficacy of the developed EMs. Utilizing data collected at 15-min intervals from Small Prespa Lake (SPL) in Greece, the models employed hourly Chl-a concentration lag times, extending up to 6 h, as models' inputs to forecast Chla (t+1). The proposed models underwent training on 70% of the dataset and were subsequently validated on the remaining 30%. Among the standalone DL models, the GRU model exhibited superior performance in Chl-a forecasting, surpassing the RNN, LSTM, and TCN models by 8%, 2%, and 2%, respectively. Furthermore, the integration of DL models through single-objective GA and multi-objective NSGA-II optimization algorithms yielded hybrid models adept at effectively forecasting both low and high Chl-a concentrations. The ensemble model based on NSGA-II outperformed standalone DL models as well as the GA-based model across a range of evaluation indices. For instance, considering the R-squared metric, the study's findings demonstrated that the EM-NSGA-II stands out with exceptional effectiveness compared to DL and EM-GA models, showcasing improvements of 14% (RNN), 8% (LSTM), 6% (GRU), 8% (TCN), and 3% (EM-GA) during the testing phase.

Dendritic Cell Density and Morphology Can Be Used to Differentiate Vernal Keratoconjunctivitis from Allergic Conjunctivitis.

The aim of the study was to compare the distribution of corneal and conjunctival epithelial dendritic cells (DCs) in vernal keratoconjunctivitis (VKC), allergic conjunctivitis (AC), and non-allergic controls to examine if the allergy type causes differences in immune cell activation. The prospective study included 60 participants: 20 with VKC, 20 with AC, and 20 non-allergic controls. In vivo confocal microscopy was performed on the right eye. The locations scanned included the corneal centre, inferior whorl, corneal periphery, corneal limbus, and bulbar conjunctiva. The DCs were counted manually, and their morphology was assessed for the largest cell body size, the presence of dendrites, and the presence of long and thick dendrites. The DC density was higher in VKC and AC compared to non-allergic group at all locations (p ≤ 0.01) except at the inferior whorl. The DC density in VKC participants was significantly higher than in AC at the limbus (p < 0.001) but not at other locations. Both the AC and the VKC group had larger DC bodies at the corneal periphery and limbus compared to the non-allergic group (p ≤ 0.03). The study found a higher proportion of participants with DCs exhibiting long dendrites at both the corneal periphery in AC (p = 0.01) and at the corneal centre, periphery, and limbus in VKC, compared to the non-allergic group (p ≤ 0.001). In conclusion, a higher DC density at the limbus may be a marker of more severe VKC. DCs with larger cell bodies and a greater proportion of participants with DCs displaying long dendrites can be potential markers to differentiate allergy from non-allergy, and more severe forms of allergy from milder forms.

Risk of Major Adverse Cardiovascular Events in Immune-Mediated Inflammatory Disorders on Biologics and Small Molecules: Network Meta-Analysis.

BACKGROUND AND AIMS: Recent studies raise concern for increased risk of major adverse cardiovascular events (MACE) with Janus kinase (JAK) inhibitors used to treat immune-mediated inflammatory disorders (IMIDs). We aimed to examine MACE risk with licensed biologics and small molecules used commonly between IMIDs: inflammatory bowel disease, rheumatoid arthritis, psoriasis/psoriatic arthritis, and ankylosing spondylitis. METHODS: Data were obtained from systematic searches (from inception to May 31, 2022) in PubMed, Embase, Ovid Medline, Scopus, Cochrane Central, and ClinicalTrials.gov. Studies that assessed a predefined MACE (myocardial infarction, cerebrovascular accident, unstable angina, cardiovascular death, or heart failure) risk in those ≥18 years of age with IMIDs treated with anti-interleukin (IL)-23 antibodies, anti-IL-12/23, anti-tumor necrosis factor α antibodies (anti-TNF-α), or JAK inhibitors were included in a network meta-analysis using a random-effects model with pooled odds ratios (ORs) reported with 95% credible intervals (CrIs) by drug class and disease state. RESULTS: Among 3528 studies identified, 40 (36 randomized controlled trials and 4 cohort studies) were included in the systematic review, comprising 126,961 patients with IMIDs. Based on network meta-analysis of randomized controlled trials, regardless of disease state, anti-TNF-α (OR, 2.49; 95% CrI, 1.14-5.62), JAK inhibitors (OR, 2.64; 95% CrI, 1.26-5.99), and anti-IL-12/23 (OR, 3.15; 95% CrI, 1.01-13.35) were associated with increased MACE risk compared with placebo. There was no significant difference in the magnitude of the MACE risk between classes or based on IMID type. CONCLUSIONS: Anti-IL-12/23, JAK inhibitors, and anti-TNF-α were associated with higher risk of MACE compared with placebo. The magnitude of the increased MACE risk was not different by IMID type. These results require confirmation in larger prospective studies.

The effects of L-carnitine supplementation on inflammatory and anti-inflammatory markers in adults: a systematic review and dose-response meta-analysis.

L-carnitine supplementation may be beneficial in improving inflammatory conditions and reducing the level of inflammatory cytokines. Therefore, according to the finding of randomized controlled trials (RCTs), the systematic review and meta-analysis aimed to investigate the effect of L-carnitine supplementation on inflammation in adults. To obtain acceptable articles up to October 2022, a thorough search was conducted in databases including PubMed, ISI Web of Science, the Cochrane Library, and Scopus. A random-effects model was used to estimate the weighted mean difference (WMD). We included the 48 RCTs (n = 3255) with 51 effect sizes in this study. L-carnitine supplementation had a significant effect on C-reactive protein (CRP) (p < 0.001), interleukin-6 (IL-6) (p = 0.001), tumor necrosis factor-α (TNF-α) (p = 0.002), malondialdehyde (MDA) (p = 0.001), total antioxidant capacity (TAC) (p = 0.029), alanine transaminase (ALT) (p < 0.001), and aspartate transaminase (AST) (p < 0.001) in intervention, compared to the placebo group. Subgroup analyses showed that L-carnitine supplementation had a lowering effect on CRP and TNF-α in trial duration ≥ 12 weeks in type 2 diabetes and BMI ≥ 25 kg/m2. L-carnitine supplementation reduced ALT levels in overweight and normal BMI subjects at any trial dose and trial duration ≥ 12 weeks and reduced AST levels in overweight subjects and trial dose ≥ 2 g/day. This meta-analysis revealed that L-carnitine supplementation effectively reduces the inflammatory state by increasing the level of TAC and decreasing the levels of CRP, IL-6, TNF-α and MDA in the serum.

The effects of acarbose treatment on cardiovascular risk factors in impaired glucose tolerance and diabetic patients: a systematic review and dose-response meta-analysis of randomized clinical trials.

Acarbose (ACB) seems to be an effective drug in the management of cardiovascular risk factors. However, no previous meta-analysis of randomized controlled trials (RCTs) has been done to evaluate the effects of ACB on cardiovascular risk factors on impaired glucose tolerance (IGT), type 2 diabetes mellitus (T2D), and type 1 diabetes mellitus (T1D). We comprehensively searched electronic databases including Scopus, Web of Science, and PubMed for RCTs for related keywords up to September 2022. A random-effects model was used to estimate the weighted mean difference (WMD) and 95% confidence interval (CI). The pooled analysis demonstrated that ACB treatment had a significant effect on fasting blood glucose (FBG) (WMD = -3.55 mg/dL; 95%CI: -6.29, -0.81; p = 0.011), fasting insulin (WMD = -6.73 pmoL/L; 95%CI: -10.37, -3.10; p < 0.001), HbA1c [WMD = -0.32%; 95%CI: -0.45, -0.20; p < 0.001], body weight (WMD = -1.25 kg; 95%CI: -1.79, -0.75; p < 0.001), body mass index (BMI) (WMD = -0.64 kg/m2; 95%CI: -0.92, -0.37; p < 0.001), tumor necrosis factor-alpha (TNF-α) (WMD = -2.70 pg/mL, 95%CI: -5.25, -0.16; p = 0.037), leptin (WMD = -1.58 ng/mL; 95%CI: -2.82, -0.35; p = 0.012), alanine transaminase (ALT) (WMD = 0.71 U/L; 95%CI: -0.31, 1.85; p = 0.164), triglyceride (TG) (WMD = -13.89 mg/dL; 95%CI: -20.69, -7.09; p < 0.001), total cholesterol (TC) (WMD = -2.26 mg/dL; 95%CI: -4.18, -0.34; p = 0.021), systolic blood pressure (SBP) (WMD = -1.29 mmHg; 95%CI: -2.44, -0.15; p = 0.027), and diastolic blood pressure (DBP) (WMD = 0.02 mmHg; 95%CI: -0.41, 0.45; p = 0.925) in an intervention group, compared with a placebo group. The non-linear dose-response analysis showed that ACB reduces the TC in trial duration by >50 weeks, and 180 mg/day is more effective for the decrement of CRP. ACB can improve lipid profiles, glycemic indices, anthropometric indices, and inflammatory markers in T2D, T1D, and IGT patients.

Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis.

Purpose: The aim of this study was to conduct a scoping review and meta-analysis to provide overall estimates of the recall and precision of artificial intelligence for detection and segmentation using oral and maxillofacial cone-beam computed tomography (CBCT) scans. Materials and Methods: A literature search was done in Embase, PubMed, and Scopus through October 31, 2022 to identify studies that reported the recall and precision values of artificial intelligence systems using oral and maxillofacial CBCT images for the automatic detection or segmentation of anatomical landmarks or pathological lesions. Recall (sensitivity) indicates the percentage of certain structures that are correctly detected. Precision (positive predictive value) indicates the percentage of accurately identified structures out of all detected structures. The performance values were extracted and pooled, and the estimates were presented with 95% confidence intervals (CIs). Results: In total, 12 eligible studies were finally included. The overall pooled recall for artificial intelligence was 0.91 (95% CI: 0.87-0.94). In a subgroup analysis, the pooled recall was 0.88 (95% CI: 0.77-0.94) for detection and 0.92 (95% CI: 0.87-0.96) for segmentation. The overall pooled precision for artificial intelligence was 0.93 (95% CI: 0.88-0.95). A subgroup analysis showed that the pooled precision value was 0.90 (95% CI: 0.77-0.96) for detection and 0.94 (95% CI: 0.89-0.97) for segmentation. Conclusion: Excellent performance was found for artificial intelligence using oral and maxillofacial CBCT images.