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Bochdalek hernias are rare diaphragmatic hernias most commonly seen in pediatric populations. Adults with this condition may be asymptomatic or present with gastrointestinal symptoms such as abdominal pain, pressure, choking, or dysphagia. Computed tomography imaging is a gold standard in diagnosing the condition. The definitive treatment is surgery, recommended and encouraged for asymptomatic patients as well to reduce the risk of future complications. Whilst the approach to surgical management differs on a case-by-case basis, the main goal is to reduce the herniating organ and repair the defect. It is important to note that in severe cases, intestinal obstruction and strangulation may occur. We present a unique case of this very phenomenon in a patient diagnosed and treated as a case of strangulated Bochdalek hernia. We aim to highlight the importance of diagnosing this condition as clinical symptoms may be non-specific, and rapid surgical intervention is necessary.

Unit commitment (UC) is an optimization problem used to determine the operation schedule of the generating units at every hour interval with varying loads under different constraints and environments. Many algorithms have been invented in the past five decades for optimization of the UC problem, but still researchers are working in this field to find new hybrid algorithms to make the problem more realistic. The importance of UC is increasing with the constantly varying demands. Therefore, there is an urgent need in the power sector to keep track of the latest methodologies to further optimize the working criterions of the generating units. This paper focuses on providing a clear review of the latest techniques employed in optimizing UC problems for both stochastic and deterministic loads, which has been acquired from many peer reviewed published papers. It has been divided into many sections which include various constraints based on profit, security, emission and time. It emphasizes not only on deregulated and regulated environments but also on renewable energy and distributed generating systems. In terms of contributions, the detailed analysis of all the UC algorithms has been discussed for the benefit of new researchers interested in working in this field.

Background and Objectives: Factors determining bone mineral (BM) loss in rheumatoid arthritis (RA) are not well known. This study aimed to determine the occurrence and predictors of BM loss in the young premenopausal women with RA. Methods: Ninety-six females with RA and 90 matched controls underwent clinical, biochemical, BM density (BMD), and body composition assessments. RA disease activity was assessed using disease activity score-28 (DAS-28) and hand X-ray. Results: In the young premenopausal females with RA having median symptom and treatment duration of 30 (18-60) and 4 (2-12) months, respectively, with moderate disease activity (DAS-28, 4.88 ± 1.17), occurrence of osteoporosis and osteopenia was 7.29% and 25% at spine, 6.25% and 32.29% at hip, and 17.7% and 56.25% at wrist, respectively (significantly higher than controls). RA patients had lower BMD at total femur, lumbar spine (LS), radius total, and radius ultra-distal. Total lean mass (LM) and BM content were significantly lower in RA (P = 0.022 and <0.001, respectively). In RA, BMD at majority of sites (LS, neck of femur, greater trochanter, radius total, and radius 33%) had the strongest positive correlation with LM followed by body fat percent. RA patients with most severe disease had lowest BMD at different sites and lowest LM. Stepwise linear regression revealed LM followed by DAS-28 to be best predictors of BMD. RA patients receiving glucocorticoids did not have significantly different BMDs from patients not taking glucocorticoids. Interpretation and Conclusion: BM loss is a significant problem in the young premenopausal women with recent-onset RA. LM and disease severity were the best predictors of BMD.

Lung cancer is one of the most lethal forms of cancer, with a very high mortality rate. The precise pathophysiology of lung cancer is not well understood, and pertinent information regarding the initiation and progression of lung cancer is currently a crucial area of scientific investigation. Enhanced knowledge about the disease will lead to the development of potent therapeutic interventions. Extracellular vesicles (EVs) are membrane-bound heterogeneous populations of cellular entities that are abundantly produced by all cells in the human body, including the tumor cells. A defined class of EVs called small Extracellular Vesicles (sEVs or exosomes) carries key biomolecules such as RNA, DNA, Proteins and Lipids. Exosomes, therefore, mediate physiological activities and intracellular communication between various cells, including constituent cells of the tumor microenvironment, namely stromal cells, immunological cells, and tumor cells. In recent years, a surge in studying tumor-associated non-coding RNAs (ncRNAs) has been observed. Subsequently, studies have also reported that exosomes abundantly carry different species of ncRNAs and these exosomal ncRNAs are functionally involved in cancer initiation and progression. Here, we discuss the function of exosomal ncRNAs, such as miRNAs and long non-coding RNAs, in the pathophysiology of lung tumors. Further, the future application of exosomal-ncRNAs in clinics as biomarkers and therapeutic targets in lung cancer is also discussed due to the multifaceted influence of exosomes on cellular physiology.

Recent growth in the capabilities of machine learning models has increased the computational complexity for inference using these models. As a result, it has become increasingly difficult for resource-constrained IoT clients to use the models on-device. With distributed computing, these IoT clients have been able to offload their collected data to remote servers employing these complex models for data analysis. However, a myriad of privacy concerns have ensued from the sharing of data between collaborating computing nodes. Privacy-Preserving Computation (PPC) schemes have emerged as very effective candidates to tackle these concerns. The inherent computational complexity of these schemes, however, make it infeasible to implement them on IoT clients operating under strict energy budgets. Specialized hardware accelerators have bridged the gap between the complex algorithms driving these schemes and their realization at the edge. Furthermore, with the constant evolution of the schemes and their prescribed parameters to counter the growing capabilities of adversaries, it becomes necessary to introduce some level of flexibility to adapt to these changes. In this thesis, we consider two frontrunner candidates for Privacy-Preserving Computations, namely Fully Homomorphic Encryption (FHE) and Threshold Signatures (TSS). We propose hardware architectures that support the optimized implementation of these schemes while staying within the energy budgets of IoT clients at the edge. For acceleration of FHE schemes, we exploit mathematical optimizations like the Residue Number System and present an optimized memory configuration. We achieve up to an order of magnitude reduction in encryption energy in post-silicon evaluation compared to a similar prior work. We also present an SoC accelerator for the FROST Threshold Signature Scheme through optimized implementation of the underlying Elliptic Curve primitives. High level key generation and signing operations are achieved with hardware-software co-design through tight coupling of the Elliptic Curve accelerator with a RISC-V CPU. To address the need for flexibility in design, we present an embedded FPGA SoC with fused ALU tiles and a shared scratchpad memory, providing tight coupling with a RISC-V CPU. We perform silicon evaluations of the architecture and demonstrate a wide range of applications along with improvements in performance and energy efficiency over their software counterparts. Additionally, we propose modifications to the eFPGA fabric to accommodate the aforementioned PPC schemes and perform a design space exploration of performance and area overheads by tuning the fabric parameters. Through this, we aim to improve the life cycle of hardware deployed at the edge, effectively future-proofing them against changes to the PPC schemes we are targeting. Finally, we do a study on the cost of flexibility by comparing the ASIC and eFPGA implementations of the PPC schemes.

A middle-aged male patient developed acute back pain and a lumbar vertebral lesion following trivial physical trauma. The lesion was considered as tuberculous on vertebral x-rays and MRI. After biopsy of the lesion and spinal fixation, the patient was kept on empirical antituberculous treatment (ATT) to which he did not respond. On re-evaluation he was diagnosed to have an Andersson lesion in ankylosing spondylitis (AS). ATT was stopped and he was successfully managed by rest, steroids, methotrexate and sulfasalazine. A careful look at the patient’s plain x-ray spine and awareness about the lesion can avoid misdiagnosis of this characteristic vertebral lesion found in AS.

ObjectiveTo develop and validate revised classification criteria for eosinophilic granulomatosis with polyangiitis (EGPA).MethodsPatients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in five phases: (1) identification of candidate criteria items using consensus methodology, (2) prospective collection of candidate items present at the time of diagnosis, (3) data-driven reduction of the number of candidate items, (4) expert panel review of cases to define the reference diagnosis and (5) derivation of a points-based risk score for disease classification in a development set using least absolute shrinkage and selection operator logistic regression, with subsequent validation of performance characteristics in an independent set of cases and comparators.ResultsThe development set for EGPA consisted of 107 cases of EGPA and 450 comparators. The validation set consisted of an additional 119 cases of EGPA and 437 comparators. From 91 candidate items, regression analysis identified 11 items for EPGA, 7 of which were retained. The final criteria and their weights were as follows: maximum eosinophil count ≥1×109/L (+5), obstructive airway disease (+3), nasal polyps (+3), cytoplasmic antineutrophil cytoplasmic antibody (ANCA) or anti-proteinase 3–ANCA positivity (−3), extravascular eosinophilic predominant inflammation (+2), mononeuritis multiplex/motor neuropathy not due to radiculopathy (+1) and haematuria (−1). After excluding mimics of vasculitis, a patient with a diagnosis of small- or medium-vessel vasculitis could be classified as having EGPA if the cumulative score was ≥6 points. When these criteria were tested in the validation data set, the sensitivity was 85% (95% CI 77% to 91%) and the specificity was 99% (95% CI 98% to 100%).ConclusionThe 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology Classification Criteria for Eosinophilic Granulomatosis with Polyangiitis demonstrate strong performance characteristics and are validated for use in research.

ObjectiveTo develop and validate revised classification criteria for granulomatosis with polyangiitis (GPA).MethodsPatients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in five phases: (1) identification of candidate criteria items using consensus methodology, (2) prospective collection of candidate items present at the time of diagnosis, (3) data-driven reduction of the number of candidate items, (4) expert panel review of cases to define the reference diagnosis and (5) derivation of a points-based risk score for disease classification in a development set using least absolute shrinkage and selection operator logistic regression, with subsequent validation of performance characteristics in an independent set of cases and comparators.ResultsThe development set for GPA consisted of 578 cases of GPA and 652 comparators. The validation set consisted of an additional 146 cases of GPA and 161 comparators. From 91 candidate items, regression analysis identified 26 items for GPA, 10 of which were retained. The final criteria and their weights were as follows: bloody nasal discharge, nasal crusting or sino-nasal congestion (+3); cartilaginous involvement (+2); conductive or sensorineural hearing loss (+1); cytoplasmic antineutrophil cytoplasmic antibody (ANCA) or anti-proteinase 3 ANCA positivity (+5); pulmonary nodules, mass or cavitation on chest imaging (+2); granuloma or giant cells on biopsy (+2); inflammation or consolidation of the nasal/paranasal sinuses on imaging (+1); pauci-immune glomerulonephritis (+1); perinuclear ANCA or antimyeloperoxidase ANCA positivity (−1); and eosinophil count ≥1×109 /L (−4). After excluding mimics of vasculitis, a patient with a diagnosis of small- or medium-vessel vasculitis could be classified as having GPA if the cumulative score was ≥5 points. When these criteria were tested in the validation data set, the sensitivity was 93% (95% CI 87% to 96%) and the specificity was 94% (95% CI 89% to 97%).ConclusionThe 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for GPA demonstrate strong performance characteristics and are validated for use in research.

ObjectiveTo develop and validate updated classification criteria for giant cell arteritis (GCA).MethodsPatients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in six phases: (1) identification of candidate items, (2) prospective collection of candidate items present at the time of diagnosis, (3) expert panel review of cases, (4) data‐driven reduction of candidate items, (5) derivation of a points‐based risk classification score in a development data set and (6) validation in an independent data set.ResultsThe development data set consisted of 518 cases of GCA and 536 comparators. The validation data set consisted of 238 cases of GCA and 213 comparators. Age ≥50 years at diagnosis was an absolute requirement for classification. The final criteria items and weights were as follows: positive temporal artery biopsy or temporal artery halo sign on ultrasound (+5); erythrocyte sedimentation rate ≥50 mm/hour or C reactive protein ≥10 mg/L (+3); sudden visual loss (+3); morning stiffness in shoulders or neck, jaw or tongue claudication, new temporal headache, scalp tenderness, temporal artery abnormality on vascular examination, bilateral axillary involvement on imaging and fluorodeoxyglucose–positron emission tomography activity throughout the aorta (+2 each). A patient could be classified as having GCA with a cumulative score of ≥6 points. When these criteria were tested in the validation data set, the model area under the curve was 0.91 (95% CI 0.88 to 0.94) with a sensitivity of 87.0% (95% CI 82.0% to 91.0%) and specificity of 94.8% (95% CI 91.0% to 97.4%).ConclusionThe 2022 American College of Rheumatology/EULAR GCA classification criteria are now validated for use in clinical research.

ObjectiveTo develop and validate classification criteria for microscopic polyangiitis (MPA).MethodsPatients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in five phases: (1) identification of candidate items using consensus methodology, (2) prospective collection of candidate items present at the time of diagnosis, (3) data-driven reduction of the number of candidate items, (4) expert panel review of cases to define the reference diagnosis and (5) derivation of a points-based risk score for disease classification in a development set using least absolute shrinkage and selection operator logistic regression, with subsequent validation of performance characteristics in an independent set of cases and comparators.ResultsThe development set for MPA consisted of 149 cases of MPA and 408 comparators. The validation set consisted of an additional 142 cases of MPA and 414 comparators. From 91 candidate items, regression analysis identified 10 items for MPA, 6 of which were retained. The final criteria and their weights were as follows: perinuclear antineutrophil cytoplasmic antibody (ANCA) or anti-myeloperoxidase-ANCA positivity (+6), pauci-immune glomerulonephritis (+3), lung fibrosis or interstitial lung disease (+3), sino-nasal symptoms or signs (−3), cytoplasmic ANCA or anti-proteinase 3 ANCA positivity (−1) and eosinophil count ≥1×109/L (−4). After excluding mimics of vasculitis, a patient with a diagnosis of small- or medium-vessel vasculitis could be classified as having MPA with a cumulative score of ≥5 points. When these criteria were tested in the validation data set, the sensitivity was 91% (95% CI 85% to 95%) and the specificity was 94% (95% CI 92% to 96%).ConclusionThe 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for MPA are now validated for use in clinical research.