Surgical Strategies for Ingrown Toenails: A Comprehensive Review of Techniques, Outcomes, and Advancements.

Ingrown toenail (IGTN), known as onychocryptosis or unguis incarnatus, is a painful condition affecting the big toe, with symptoms including pain, inflammation, and infection. This review explores surgical options for IGTN, categorized into altering the nail plate or diminishing periungual tissues. Conservative treatments alleviate early-stage symptoms, while surgical interventions are reserved for severe cases. Various surgical techniques are discussed, such as the Winograd technique, Vandenbos procedure, chemical matricectomy, radiofrequency ablation, bipolar diathermy, carbon dioxide laser ablation, Zadik's procedure, Howard-Dubois procedure, Super U procedure, Noël's procedure, knot technique, and toenail paronychium flap. The choice of procedure depends on the severity and recurrence of IGTN.

Recent synthetic strategies of small heterocyclic organic molecules with optoelectronic applications: a review.

Over the past few years, there have been tremendous developments in the design and synthesis of organic optoelectronic materials with appealing applications in device fabrication of organic light-emitting diodes, superconductors, organic lasers, organic field-effect transistors, clean energy-producing organic solar cells, etc. There is an increasing demand for the synthesis of green, highly efficient organic optoelectronic materials to cope with the issue of efficiency roll-off in organic semiconductor-based devices. This review systematically summarized the recent progress in the design and synthesis of small organic molecules having promising optoelectronic properties for their potential applications in optoelectronic devices during the last 10-year range (2010-early 2021).

A hybrid dependency-based approach for Urdu sentiment analysis.

In the digital age, social media has emerged as a significant platform, generating a vast amount of raw data daily. This data reflects the opinions of individuals from diverse backgrounds, races, cultures, and age groups, spanning a wide range of topics. Businesses can leverage this data to extract valuable insights, improve their services, and effectively reach a broader audience based on users' expressed opinions on social media platforms. To harness the potential of this extensive and unstructured data, a deep understanding of Natural Language Processing (NLP) is crucial. Existing approaches for sentiment analysis (SA) often rely on word co-occurrence frequencies, which prove inefficient in practical scenarios. Identifying this research gap, this paper presents a framework for concept-level sentiment analysis, aiming to enhance the accuracy of sentiment analysis (SA). A comprehensive Urdu language dataset was constructed by collecting data from YouTube, consisting of various talks and reviews on topics such as movies, politics, and commercial products. The dataset was further enriched by incorporating language rules and Deep Neural Networks (DNN) to optimize polarity detection. For sentiment analysis, the proposed framework employs predefined rules to trigger sentiment flow from words to concepts, leveraging the dependency relations among different words in a sentence based on Urdu language grammatical rules. In cases where predefined patterns are not triggered, the framework seamlessly switches to its sub-symbolic counterpart, passing the data to the DNN for sentence classification. Experimental results demonstrate that the proposed framework surpasses state-of-the-art approaches, including LSTM, CNN, SVM, LR, and MLP, achieving an improvement of 6-7% on Urdu dataset. In conclusion, this research paper introduces a novel framework for concept-level sentiment analysis of Urdu language data sourced from social media platforms. By combining language rules and DNN, the proposed framework demonstrates superior performance compared to existing methodologies, showcasing its effectiveness in accurately analyzing sentiment in Urdu text data.

Treatment of eculizumab refractory paroxysmal nocturnal hemoglobinuria: A systematic review about current treatment options and future direction.

Objectives: C5 inhibitors such as eculizumab and ravulizumab are the first-line treatment in the management of paroxysmal nocturnal hemoglobinuria (PNH). However, some patients develop novel symptoms as part of their treatment with eculizumab, and the disease is termed as eculizumab refractory PNH. The aim of this study was to conduct a systematic review on the available treatment modalities for the management of eculizumab refractory PNH. Methods: Two authors independently searched two databases according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 70 studies were obtained: 4 out 70 studies were found to meet the inclusion criteria. Results: Four studies were found to meet the inclusion criteria of our study. Two studies were published in 2021 and two studies were published in 2020. All four studies were multicenter clinical trials. Two studies were phase III clinical trials, one study was a phase II clinical trial, and one study was a phase I clinical trial. Two studies were about pegcetacoplan, one was about danicopan, and one was about iptacopan. Conclusion: Based upon the findings of our systematic review, we recommend an individualized treatment plan based on the mechanism of eculizumab refractoriness and the mechanism of PNH breakthrough. This recommendation is subject to the available resources and clinical expertise available at different hospitals. More studies using study designs such as randomized controlled trials comparing multiple drugs should be performed to accurately assess the different medications and aid in designing guidelines of the management of eculizumab refractory PNH. Level of evidence: Level I.

Role of Anifrolumab in Refractory Cutaneous Manifestations of Lupus Erythematosus: A Case Series and Literature Review.

Lupus erythematosus (LE) is an autoimmune disease that presents either as a systemic (SLE) or an isolated skin disease (CLE). Currently, there is no FDA-approved medication specifically for CLE, and is treated with the same approach as SLE. We present two refractory cases of SLE with severe cutaneous manifestations unresponsive to the first-line therapy treated with anifrolumab. First, a 39-year-old Caucasian female with a known history of SLE with severe subacute CLE presented to the clinic for her refractory cutaneous symptoms. Her current regimen was hydroxychloroquine (HCQ), mycophenolate mofetil (MMF), and s/c belimumab with no improvement. Belimumab was discontinued, and she was started on anifrolumab with significant improvement. Another, a 28-year-old female with no known medical history was referred to a rheumatology clinic for elevated anti-nuclear antibody (ANA) and ribonucleoprotein (RNP) titers. She was diagnosed with SLE, and was treated with HCQ, belimumab, and MMF but failed to produce a reasonably good outcome. Hence belimumab was discontinued and anifrolumab was added instead with significant cutaneous improvement. The treatment spectrum for SLE is wide, which includes antimalarial (HCQ), oral corticosteroids (OCS), and immunosuppressants (Methotrexate-MTX, MMF, azathioprine-AZT). Anifrolumab, a type 1 IFNα receptor subunit 1 (IFNAR1) inhibitor, has been recently approved by the FDA for moderate to severe SLE while on standard therapy in August 2021. Early use of anifrolumab in moderate to severe cutaneous manifestations of SLE or CLE may result in significant improvement in patients.

Marijuana as a Cause of Diffuse Coronary Vasospasm Leading to Cardiac Arrest.

Marijuana is considered as the most popular illicit drug around the world. It has numerous cardiovascular effects with myocardial infarction (MI) being a lethal one. The negative physiological effects of marijuana are well-studied, including tachycardia, nausea, memory impairment, anxiety, panic, and arrhythmia. We present a case of cardiac arrest following marijuana use in a patient who had a normal electrocardiogram (EKG) on presentation but diffuse coronary vasospasm on left heart catheterization (LHC) with no obstructive lesion. The patient had a transient episode of ST elevation on EKG following the procedure which resolved with an increased dose of nitroglycerine drip. Synthetic cannabinoids are more potent and not detected on a regular urine drug screen (UDS). In patients with low risk for cardiovascular events, particularly young adults, presenting with symptoms of MI/cardiac arrest, marijuana-induced MI should be suspected due to the severe adverse effects of its synthetic component.

Relatively large wings facilitate life at higher elevations among Nearctic dragonflies.

Determining which traits allow species to live at higher elevations is essential to understanding the forces that shape montane biodiversity. For the many animals that rely on flight for locomotion, a long-standing hypothesis is that species with relatively large wings should better persist in high-elevation environments because wings that are large relative to the body generate more lift and decrease the aerobic costs of remaining aloft. Although these biomechanical and physiological predictions have received some support in birds, other flying taxa often possess smaller wings at high elevations or no wings at all. To test if predictions about the requirements for relative wing size at high elevations are generalizable beyond birds, we conducted macroecological analyses on the altitudinal characteristics of 302 Nearctic dragonfly species. Consistent with the biomechanical and aerobic hypotheses, species with relatively larger wings live at higher elevations and have wider elevation breadths-even after controlling for a species' body size, mean thermal conditions, and range size. Moreover, a species' relative wing size had nearly as large of an impact on its maximum elevation as being adapted to the cold. Relatively large wings may be essential to high-elevation life in species that completely depend on flight for locomotion, like dragonflies or birds. With climate change forcing taxa to disperse upslope, our findings further suggest that relatively large wings could be a requirement for completely volant taxa to persist in montane habitats.

A mobile-based system for maize plant leaf disease detection and classification using deep learning.

Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds.

Volume outcome relationship in post-esophagectomy leak: A systematic review and meta-analysis.

BACKGROUND: Anastomotic leak after esophagectomy carries important short and long-term sequelae. We conducted a systematic review and meta-analysis to determine its association with surgical volume. MATERIALS AND METHODS: A systematic literature review was performed to identify all studies reporting on anastomotic leak after esophagectomy. Studies with <100 cases were excluded. The primary outcome was post-esophagectomy anastomotic leak, while secondary outcomes were operative mortality overall and after anastomotic leak. Pooled event rates (PER) were calculated and association with annual esophagectomy volume by center was investigated. RESULTS: Of the 3,932 retrieved articles, 472 were included (n=177,566 patients). The PER of anastomotic leak was 8.91% [95%CI=8.32; 9.53%]. The PER of early mortality overall and after anastomotic leak was 2.49% [95%CI=2.27; 2.74] and 11.39% [95%CI=9.66; 13.39], respectively. Centers with <37 annual esophagectomies had a higher leak rate compared to those with ≥37 annual esophagectomies (9.58% vs. 8.34%; P=0.040). On meta-regression, surgical volume was inversely associated with the PER of esophageal leak and of early mortality. CONCLUSION: The frequency of anastomotic leaks after esophagectomy, perioperative and leak associated mortality are inversely associated with esophagectomy volume.

Ring forming transformations of ynamides via cycloaddition.

Ynamides are N-alkyne compounds bearing an electron withdrawing group at the nitrogen atom. They offer unique pathways for the construction of versatile building blocks owing to their exceptional balance between reactivity and stability. Recently several studies have been reported that explore and illustrate the synthetic potential of ynamides and ynamide-derived advanced intermediates in cycloadditions with different reaction partners to yield heterocyclic cycloadducts of synthetic and pharmaceutical value. Cycloaddition reactions of ynamides are the facile and preferable routes for the construction of structural motifs having striking importance in synthetic, medicinal chemistry, and advanced materials. In this systematic review, we highlighted the recently reported novel transformations and synthetic applications that involved the cycloaddition reaction of ynamides. The scope along with the limitations of the transformations are discussed in detail.

Is CRISPR/Cas9 a way forward to fast-track genetic improvement in commercial palms? Prospects and limits.

Commercially important palms (oil palm, coconut, and date palm) are widely grown perennial trees with tremendous commercial significance due to food, edible oil, and industrial applications. The mounting pressure on the human population further reinforces palms' importance, as they are essential crops to meet vegetable oil needs around the globe. Various conventional breeding methods are used for the genetic improvement of palms. However, adopting new technologies is crucial to accelerate breeding and satisfy the expanding population's demands. CRISPR/Cas9 is an efficient genome editing tool that can incorporate desired traits into the existing DNA of the plant without losing common traits. Recent progress in genome editing in oil palm, coconut and date palm are preliminarily introduced to potential readers. Furthermore, detailed information on available CRISPR-based genome editing and genetic transformation methods are summarized for researchers. We shed light on the possibilities of genome editing in palm crops, especially on the modification of fatty acid biosynthesis in oil palm. Moreover, the limitations in genome editing, including inadequate target gene screening due to genome complexities and low efficiency of genetic transformation, are also highlighted. The prospects of CRISPR/Cas9-based gene editing in commercial palms to improve sustainable production are also addressed in this review paper.

Palmitoylation of the Alternative Amino Terminus of the BTK-C Isoform Controls Subcellular Distribution and Signaling.

BACKGROUND: The alternative transcriptional isoform of Bruton's tyrosine kinase, BTK-C, is expressed in a wide variety of epithelial tumor types where it impacts apoptosis resistance, therapeutic escape, and glucose uptake. The initial exon in BTK-C encodes a 34 amino acid extension of the amino terminus of the canonical BTK-A isoform. Its function is unknown. MATERIALS AND METHODS: Site-directed mutagenesis, acylation assays and expression studies in cancer cell lines were used to determine the effects that the BTK-C first exon sequence has on kinase activity, subcellular localization and cell physiology. Analysis of BTK-C expression in tumors was conducted using genomic databases. RESULTS: BTK-C is palmitoylated on two cysteine residues. BTK-C localization at the plasma membrane is dependent upon phosphatidylinositol 3,4,5-triphosphate (PIP3) levels as well as palmitoylation. In epithelial cancer cells, both BTK-A and BTK-C isoforms are recruited to the plasma membrane; however, BTK-A also localizes to the nucleus whereas BTK-C has a primarily perinuclear distribution. Transcription of the BTK-C isoform is inversely correlated with expression of commonly activated breast cancer signaling receptors in breast tumors. In MDA-MB-231 cells, BTK-C expression confers modest increases in proliferation and glucose uptake rates compared to BTK-A. CONCLUSION: Palmitoylation affects localization and regulation of BTK-C in epithelial tumor cells where it functions as an important survival factor. Expression of either palmitoylated or non-palmitoylated kinase isoforms that function in PI3K signaling may be a common regulatory feature as nine other soluble kinases in the human genome possess similarly encoded alternative N-termini (ANT).

COVID-opt-aiNet: A clinical decision support system for COVID-19 detection.

Coronavirus disease (COVID-19) has had a major and sometimes lethal effect on global public health. COVID-19 detection is a difficult task that necessitates the use of intelligent diagnosis algorithms. Numerous studies have suggested the use of artificial intelligence (AI) and machine learning (ML) techniques to detect COVID-19 infection in patients through chest X-ray image analysis. The use of medical imaging with different modalities for COVID-19 detection has become an important means of containing the spread of this disease. However, medical images are not sufficiently adequate for routine clinical use; there is, therefore, an increasing need for AI to be applied to improve the diagnostic performance of medical image analysis. Regrettably, due to the evolving nature of the COVID-19 global epidemic, the systematic collection of a large data set for deep neural network (DNN)/ML training is problematic. Inspired by these studies, and to aid in the medical diagnosis and control of this contagious disease, we suggest a novel approach that ensembles the feature selection capability of the optimized artificial immune networks (opt-aiNet) algorithm with deep learning (DL) and ML techniques for better prediction of the disease. In this article, we experimented with a DNN, a convolutional neural network (CNN), bidirectional long-short-term memory, a support vector machine (SVM), and logistic regression for the effective detection of COVID-19 in patients. We illustrate the effectiveness of this proposed technique by using COVID-19 image datasets with a variety of modalities. An empirical study using the COVID-19 image dataset demonstrates that the proposed hybrid approaches, named COVID-opt-aiNet, improve classification accuracy by up to 98%-99% for SVM, 96%-97% for DNN, and 70.85%-71% for CNN, to name a few examples. Furthermore, statistical analysis ensures the validity of our proposed algorithms. The source code can be downloaded from Github: https://github.com/faizakhan1925/COVID-opt-aiNet.

Spinal cord injury after open and endovascular repair of descending thoracic and thoracoabdominal aortic aneurysms: A meta-analysis.

OBJECTIVE: An inclusive contemporary analysis of spinal cord injury (SCI) rates in patients undergoing aneurysm repair and the factors associated with complications has not been performed. METHODS: Following a systematic literature search, studies from 2008 to 2018 on repair of descending thoracic aneurysm (DTA) and thoracoabdominal aortic aneurysm (TAAA) were pooled in a meta-analysis performed using the generic inverse variance method. The primary outcome was permanent SCI. Secondary outcomes were temporary SCI, operative mortality, long-term mortality, postoperative stroke, and cerebrospinal fluid (CSF) drain-related complications. RESULTS: One-hundred sixty-nine studies (22,634 patients) were included. The pooled rate of permanent SCI was 4.5% (95% confidence interval [CI], 3.8-5.4); 3.5% (95% CI, 1.8-6.7) for DTA and 7.6% (96% CI, 6.2-9.3) for TAAA repair (P for subgroups = .02), 5.7% (95% CI, 4.3-7.5) for open repair and 3.9% (95% CI, 3.1-4.8) for endovascular repair (P for subgroups = .03). Rates for Crawford extents I, II, III, IV, and V aneurysms were 4.0% (95% CI, 3.0-5.0), 15.0% (95% CI, 10.0-22.0), 7.0% (95% CI, 6.0-9.0), 2.0% (95% CI, 2.0-4.0), and 7.0% (95% CI, 2.0-23.0) respectively (P for subgroups <.001). The pooled rates for operative mortality, late mortality at a mean follow-up of 5.0 years, stroke, and temporary SCI were 7.4% (95% CI, 6.1-9.4), 1.0% (95% CI, 0.0-1.0), 4.2% (95% CI, 3.6-4.8), and 3.7% (95% CI, 3.0-4.6), respectively. The pooled rates for severe, moderate, and minor CSF-drain related complications were 5.1% (95% CI, 2.23-11.1), 4.1% (95% CI, 0.6-22.0), and 3.6% (95% CI, 1.2-8.0) respectively. CONCLUSIONS: Despite improvement, both open and endovascular aneurysm repair remain associated with a substantial risk of permanent SCI. The risk is greater for TAAA repair, especially extent II, III, and V.

Risk Stratification in Primary Care: Value-Based Contributions of Provider Adjudication.

BACKGROUND: In primary care risk stratification, automated algorithms do not consider the same factors as providers. The process of adjudication, in which providers review and adjust algorithm-derived risk scores, may improve the prediction of adverse outcomes. OBJECTIVE: We assessed the patient factors that influenced provider adjudication behavior and evaluated the performance of an adjudicated risk model against a commercial algorithm. DESIGN: (1) Structured interviews with primary care providers (PCP) and multivariable regression analysis and (2) receiver operating characteristic curves (ROC) with sensitivity analyses. PARTICIPANTS: Primary care patients aged 18 years and older with an adjudicated risk score. APPROACH AND MAIN MEASURES: (1) Themes from structured interviews and discrete variables associated with provider adjudication behavior; (2) comparison of concordance statistics and sensitivities between risk models. KEY RESULTS: 47,940 patients were adjudicated by PCPs in 2018. Interviews revealed that, in adjudication, providers consider disease severity, presence of self-management skills, behavioral health, and whether a risk score is actionable. Provider up-scoring from the algorithmic risk score was significantly associated with patient male sex (OR 1.24, CI 1.15-1.34), age > 65 (OR 2.55, CI 2.24-2.91), Black race (1.26, CI 1.02-1.55), polypharmacy >10 medications (OR 4.87, CI 4.27-5.56), a positive depression screen (OR 1.57, CI 1.43-1.72), and hemoglobin A1c >9 (OR 1.89, CI 1.52-2.33). Overall, the adjudicated risk model performed better than the commercial algorithm for all outcomes: ED visits (c-statistic 0.689 vs. 0.684, p < 0.01), hospital admissions (c-statistic 0.663 vs. 0.649, p < 0.01), and death (c-statistic 0.753 vs. 0.721, p < 0.01). When limited to males or seniors, the adjudicated models displayed either improved or non-inferior performance compared to the commercial model. CONCLUSIONS: Provider adjudication of risk stratification improves model performance because providers have a personal understanding of their patients and are able to apply their training to clinical decision-making.

Transcriptomic and physiological analysis reveals interplay between salicylic acid and drought stress in citrus tree floral initiation.

MAIN CONCLUSION: Salicylic acid (SA) and drought stress promote more flowering in sweet orange. The physiological response and molecular mechanism underlying stress-induced floral initiation were discovered by transcriptome profiling. Numerous flowering-regulated genes were identified, and ectopically expressed CsLIP2A promotes early flowering in Arabidopsis. Floral initiation is a critical developmental mechanism associated with external factors, and citrus flowering is mainly regulated by drought stress. However, little is known about the intricate regulatory network involved in stress-induced flowering in citrus. To understand the molecular mechanism of floral initiation in citrus, flower induction was performed on potted Citrus sinensis trees under the combined treatment of salicylic acid (SA) and drought (DR). Physiological analysis revealed that SA treatment significantly normalized the drastic effect of drought stress by increasing antioxidant enzyme activities (SOD, POD, and CAT), relative leaf water content, total chlorophyll, and proline contents and promoting more flowering than drought treatment. Analysis of transcriptome changes in leaves from different treatments showed that 1135, 2728 and 957 differentially expressed genes (DEGs) were revealed in response to DR, SD (SA + DR), and SA (SA + well water) treatments in comparison with the well watered plants, respectively. A total of 2415, 2318 and 1933 DEGs were expressed in DR, SD, and SA in comparison with water recovery, respectively. Some key flowering genes were more highly expressed in SA-treated drought plants than in DR-treated plants. GO enrichment revealed that SA treatment enhances the regulation and growth of meristem activity under drought conditions, but no such a pathway was found to be highly enriched in the control. Furthermore, we focused on various hormones, sugars, starch metabolism, and biosynthesis-related genes. The KEGG analysis demonstrated that DEGs enriched in starch sucrose metabolism and hormonal signal transduction pathways probably account for stress-induced floral initiation in citrus. In addition, a citrus LIPOYLTRANSFERSAE 2A homologous (LIP2A) gene was upregulated by SD treatment. Ectopic expression of CsLIP2A exhibited early flowering in transgenic Arabidopsis. Taken together, this study provides new insight that contributes to citrus tree floral initiation under the SA-drought scenario as well as an excellent reference for stress-induced floral initiation in woody trees.

Genome-Wide Identification and Expression Profiling of the WOX Gene Family in Citrus sinensis and Functional Analysis of a CsWUS Member.

WUSCHEL-related homeobox (WOX) transcription factors (TFs) are well known for their role in plant development but are rarely studied in citrus. In this study, we identified 11 putative genes from the sweet orange genome and divided the citrus WOX genes into three clades (modern/WUSCHEL(WUS), intermediate, and ancient). Subsequently, we performed syntenic relationship, intron-exon organization, motif composition, and cis-element analysis. Co-expression analysis based on RNA-seq and tissue-specific expression patterns revealed that CsWOX gene expression has multiple intrinsic functions. CsWUS homolog of AtWUS functions as a transcriptional activator and binds to specific DNA. Overexpression of CsWUS in tobacco revealed dramatic phenotypic changes, including malformed leaves and reduced gynoecia with no seed development. Silencing of CsWUS in lemon using the virus-induced gene silencing (VIGS) system implied the involvement of CsWUS in cells of the plant stem. In addition, CsWUS was found to interact with CsCYCD3, an ortholog in Arabidopsis (AtCYCD3,1). Yeast one-hybrid screening and dual luciferase activity revealed that two TFs (CsRAP2.12 and CsHB22) bind to the promoter of CsWUS and regulate its expression. Altogether, these results extend our knowledge of the WOX gene family along with CsWUS function and provide valuable findings for future study on development regulation and comprehensive data of WOX members in citrus.