Cystic fibrosis: current treatment and future direction

Cystic fibrosis is an autosomal recessive genetic disorder, characterized by mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, leading to abnormality in the chloride channels of the mucus and sweat producing glands. Multiple organs systems are affected in this disorder, like respiratory system and gastrointestinal tract, severely impacting the patient’s quality of life, eventually leading on to several complications and death. Since the uncovering of the underlying genetic defect in cystic fibrosis (CF), our knowledge of the disease process has increased substantially, but we still lack a holistic approach to its management, which comprises of multiple facades, requiring both pharmacological and non-pharmacological or rehabilitatory approaches. So far, the therapeutic options were limited to targeting the consequences and complications of the disease, such as respiratory infection, mucus retention, pancreatic insufficiency, etc., but now several promising therapies may be able to address the underlying pathology rather than its long-term effects. This review summarizes the current and upcoming pharmacological options for CF, such as those targeting the CFTR gene defect directly, including gene editing, CFTR correctors and potentiators; drugs targeting the epithelial sodium channels (ENaC inhibitors); repositioning of some existing drugs and evaluating their role in CF; and understanding the disease better by transcriptomic approaches and the role of gut microbiota in the disease process and severity

Quasiseparable Approach to Evaluating Cubic Splines

The development of fast and efficient algorithms is crucial not only for computer scientists, but also for mathematicians and engineers as those algorithms lead to reduce complexity. Another common interest of these professionals is to construct models using existing data. This leads numerical analysts to explore interpolation techniques. One such technique is called cubic spline interpolation. In here, we will propose a cubic spline solver aiming to bridge the gap between numerical linear algebra, electrical engineering, systems engineering, sensor processing, and parallel processing. We will use quasiseparable structure to evaluate cubic splines by deriving a fast and stable algorithm. The derivation is carried through a specific factorization of the inverse of tridiagonal matrices. This factorization leads to an alternative method to solve the system of tridiagonal matrices as opposed to the existing methods. The proposed algorithm has the lowest computational complexity compared to existing algorithms

Multi-modal Extreme Classification

This paper develops the MUFIN technique for extreme classification (XC) tasks with millions of labels where datapoints and labels are endowed with visual and textual descriptors. Applications of MUFIN to product-to-product recommendation and bid query prediction over several millions of products are presented. Contemporary multi-modal methods frequently rely on purely embedding-based methods. On the other hand, XC methods utilize classifier architectures to offer superior accuracies than embedding only methods but mostly focus on text-based categorization tasks. MUFIN bridges this gap by reformulating multi-modal categorization as an XC problem with several millions of labels. This presents the twin challenges of developing multi-modal architectures that can offer embeddings sufficiently expressive to allow accurate categorization over millions of labels; and training and inference routines that scale logarithmically in the number of labels. MUFIN develops an architecture based on cross-modal attention and trains it in a modular fashion using pre-training and positive and negative mining. A novel product-to-product recommendation dataset MM-AmazonTitles-300K containing over 300K products was curated from publicly available amazon.com listings with each product endowed with a title and multiple images. On the all datasets MUFIN offered at least 3% higher accuracy than leading text-based, image-based and multi-modal techniques. Code for MUFIN is available at https://github.com/Extreme-classification/MUFI

Role of imaging in the management of thyroglossal duct cyst carcinomas (TGC-TIRADS): a single centre retrospective study over 16 years

IntroductionThyroglossal duct cyst (TGDC) is the most frequently encountered developmental anomaly in thyroid genesis with a reported incidence of 7% in the adult population. The cyst is known to develop anywhere along the pathway of thyroid descent but is more frequently seen in the infrahyoid neck in the midline. The incidence of malignancy in a TGDC is approximately 1%; a majority of these are papillary carcinomas. This study was conducted at a single tertiary care centre which spanned over a decade which adds practice changing evidence-based knowledge to existing literature on this rare entity. A comprehensive study which conclusively establishes the imaging features predictive of malignancy in TGDC carcinomas (TGDCa), the protocol for optimal management, clinical outcome and long-term survival of these patients is not available. Although TGDC carcinoma is thought to have an excellent prognosis, there is not enough data available on the long-term survival of these patients. The aim of this study was to identify whether neck ultrasound (US) can serve as an accurate imaging tool for the preoperative diagnosis of TGDC carcinomas.MethodsWe accessed the electronic medical records of 86 patients with TGDC between January 2005 to December 2021. Of these, 22 patients were detected with TGDC papillary carcinoma on histopathologic examination. Relevant imaging, treatment and follow up information for all cases of TGDC carcinoma were retrospectively reviewed. We compared US characteristics predictive of malignancy across outcomes groups; malignant vs benign using the Chi-square test. Based on the results, a TGC-TIRADS classification was proposed with calculation of the percentage likelihood of malignancy for each category.ResultsCompared to benign TGDCs, malignant TGDCs were more likely to present with following US characteristics: irregular or lobulated margins (90.40 vs. 38.10%), solid-cystic composition (61.90 vs. 17.07%), internal vascularity (47.62 vs. 4.88 %), internal calcification (76.19 vs. 7.32 %) (each p value < 0.005). Calcifications and internal vascularity were the most specific while irregular/lobulated margins were the most sensitive feature for malignancy. AUC under the ROC curve was 0.88. Allpatients were operated and were disease free at the end of 5 years or till the recent follow up.DiscussionUS is the imaging modality of choice for pre-operative diagnosis of TGDC carcinoma. Thepre-operative diagnosis and risk stratification of thyroglossal lesions will be aided by the application of the proposed TGC-TIRADS classification, for which the percentage likelihood of malignancy correlated well with the results in our study. Sistrunk procedure is adequate for isolated TGDC carcinoma; suspicious neck nodes on imaging also necessitates selective nodal dissection. Papillary carcinomas have an excellent prognosis with low incidence of disease recurrence

A comparative evaluation of natural and artificial scaffolds in regenerative endodontics: A clinical study

Aim: To evaluate and compare the regenerative potential of natural autologous scaffolds (blood clot and platelet rich fibrin [PRF]) with artificial scaffolds (commercially available collagen and poly-lactic-co-glycolic acid [PLGA] polymer) in inducing apexogenesis in necrotic immature permanent teeth. Materials and Methods: Necrotic immature permanent maxillary incisors with or without radiographic evidence of periapical lesion were included. Access opening was done under rubber dam isolation. Canal disinfection was done using minimal instrumentation, copious irrigation, and triple antibiotic paste as interappointment medicament for 4 weeks. After 4 weeks, asymptomatic teeth were divided into four groups on the basis of scaffolds used for revascularization procedure: Group I (blood clot); Group II (PRF); Group III (collagen); Group IV (PLGA). The clinical and radiographic evaluations of teeth were done at 6 and 12 months after the procedure and compared with baseline records. Result: Clinically, patients were completely asymptomatic throughout the study period. Radiographically, all cases showed improvement in terms of periapical healing, apical closure, root lengthening, and dentinal wall thickening. PRF and collagen gave better results than blood clot and PLGA in terms of periapical healing, apical closure, and dentinal wall thickening. Conclusion: Revascularization procedure is more effective and conservative over apexification in the management of necrotic immature permanent teeth. This study has shown that PRF and collagen are better scaffolds than blood clot and PLGA for inducing apexogenesis in immature necrotic permanent teeth

19 Laryngoscopy in Neonates and Infants presenting with stridor in tertiary care hospital

<p>23A | 36 | JAN 2021 | IJABMS</p><p>Medical Journal Research Article</p><p>19 Laryngoscopy in Neonates and Infants presenting with stridor in tertiary care hospital</p&gt

Remote Sensing Imagery Segmentation: A Hybrid Approach

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity

Remote Sensing Imagery Segmentation: A Hybrid Approach

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity

Granular cell tumour of the pancreas: A case report and systematic review

Purpose: Granular cell tumours (GCTs) of the pancreas are mostly benign and exceptionally rare, with no unique identifying radiological features. Following a case discussion of a patient with GCT, a comprehensive review of available literature was conducted to identify the common diagnostic features associated with GCT. Methods: Following a case report identifed in our institution, a systematic review was conducted by two authors in accordance with Preferred Reporting Items for Systematic review and Meta-Analysis protocols (PRISMA) guidelines. Databases MEDLINE, EMBASE, Scopus, World of Science, and grey literature were searched on August 2021. Inclusion criteria were histopathology diagnosed granular cell tumour of the pancreas. Results: A 37-year-old male presented with 1 month of abdominal pain and an MRI demonstrating a dilated main pancreatic duct, distal parenchymal atrophy, but no focal lesion. Repeat MRI at 6 months re-demonstrated similar fndings and subsequent endoscopic ultrasound was suspicious for main duct IPMN. Following multidisciplinary team discussion, a spleenpreserving distal pancreatectomy was performed. Histopathology demonstrated granular cell tumour with cells difusely positive for S100 and no malignant transformation. 11 case reports were identifed in the literature with diagnosis confrmed on tissue histopathology based on positive immunohistochemical staining for S-100 protein. Eight patients presented with gastrointestinal symptoms with abdominal pain the main presenting complaint (50%). 10 patients underwent CT with portal venous contrast and all underwent endoscopic examination. Imaging fndings were similar in fve studies for EUS which demonstrated a hypoechoic lesion with homogenous appearance. On non-contrast CT GCT was iso-enhancing, and with portal venous contrast demonstrated hypo-enhancement that gradually enhanced on late phases. Pre-operative diagnosis of pancreatic carcinoma was described in six cases based on imaging and biopsy, resulting in progression to surgical resection. Nine patients were managed surgically and no complications identifed on follow-up (6–52 months). Conclusion: The currently proposed management pathway includes EUS with biopsy and CT, and surgical resection recommended due to malignancy risk. Improved sample collection with EUS-FNA and microscopic assessment utilising S-100 immunohistochemistry may improve pre-operative diagnosis. Limitations include rare numbers in reported literature and short follow-up not allowing an assessment of GCT’s natural history and malignancy risk. Additional cases would expand the current dataset of GCTs of the pancreas, so that surgical resection may be avoided in the future

Somatostatin receptor SSTR-2a expression is a stronger predictor for survival than Ki-67 in pancreatic neuroendocrine tumors

Somatostatin receptors (SSTR) are commonly expressed by neuroendocrine tumors. Expression of SSTR-2a and SSTR-5 may impact symptomatic management; however, the impact on survival is unclear. The aim of this study is to correlate SSTR-2a and SSTR-5 expression in pancreatic neuroendocrine tumors (PNETs) with survival. This study is designed to determine the prognostic significance of somatostatin receptors SSTR-2a and SSTR-5 in PNETs. This retrospective cohort study included cases of resected PNETs between 1992 and 2014. Clinical data, histopathology, expression of SSTR and Ki-67 by immunohistochemistry, and long-term survival were analyzed. A total of 99 cases were included in this study. The mean age was 57.8 years (18-87 years) and median tumor size was 25 mm (range 8-160 mm). SSTR-2a and SSTR-5 expression was scored as negative (n = 19, 19.2%; n = 75, 75.8%, respectively) and positive (n = 80, 80.1%; n = 24, 24.2%). The median follow-up was 49 months. SSTR-2a expression was associated with improved overall survival, with cumulative survival rates at 1, 3, and 5 years being 97.5%, 91.5%, and 82.9%, respectively. Univariate analysis demonstrated better survival in SSTR-2a positive patients (log rank P = 0.04). SSTR-5 expression was not associated with survival outcomes (log rank P = 0.94). Multivariate analysis showed that positive SSTR-2a expression is a stronger prognostic indicator for overall survival [Hazard Ratio (HR): 0.2, 95% Confidence interval (CI): 0.1-0.8] compared to high Ki-67 (HR: 0.8, 95% CI: 0.1-5.7). Expression of SSTR-2a is an independent positive prognostic factor for survival in PNETs.6 page(s