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Background: Frozen shoulder (FS) is a debilitating inflammatory condition affecting the shoulder capsule that causes significant pain and stiffness. Its etiology, pathophysiology, and treatment remain poorly understood. Although regarded as self-limiting, FS can have profound implications on the activities of daily living and usually takes 1-4 years to resolve on its own accord. In recalcitrant or severe cases where active range of motion (AROM) is extensively restricted, an arthroscopic 360° release may be performed. The aim of this study is to evaluate the clinical outcomes following the FS 360° arthroscopic release. Methods: An observational prospective cohort study was conducted assessing patient-reported outcome measures (PROMs) in patients who underwent the 360° arthroscopic release between July 2013 and January 2019. Various questionnaires were used to evaluate their shoulder preoperatively and at 2 weeks, 6 weeks, 3 months, 6 months, 12 months, and 24 months postoperatively. Relevant PROMs included the Oxford Shoulder Score; Western Ontario Shoulder Instability Index; Disabilities of the Arm, Shoulder, and Hand; Constant-Murley Score; American Shoulder and Elbow Surgeons score; and general measures of pain intensity (visual analog scale) and well-being (EQ-5D-3L). AROM movements included forward flexion, abduction, external rotation, internal rotation, as well as external and internal rotation at 90° of abduction. Results: Fifty consented patients underwent the arthroscopic FS 360° release. The mean age was 52.1 ± 7.7 years (range 35-72), and mean body mass index was 27.1 ± 4.7 kg/m2 (range 19.5-37.5). All PROMs, AROM movements, patient satisfaction, and EQ-5D-3L scores improved significantly between preoperative and 24-month time points (P .05). No complications or reoperations were reported. Conclusion: The arthroscopic 360° release is an effective and safe treatment modality for severe or recalcitrant FS. Statistically and clinically significant improvements in AROM and PROMs (Oxford Shoulder Score; Western Ontario Shoulder Instability Index; Disabilities of the Arm, Shoulder, and Hand; Constant-Murley Score; and American Shoulder and Elbow Surgeons score) occurred shortly after the surgery and progressively improved from 2 weeks to 24 months postoperatively, with the operative shoulder achieving similar range of motion as the nonpathological contralateral shoulder at 24 months.

Tenodesis of the long head of biceps is a common shoulder surgical procedure. Tenodesis can be performed either arthroscopically or open and within the glenohumeral joint, within the bicipital groove, or below the pectoralis major tendon insertion. Arthroscopic tenodesis of the biceps tendon reduces the risk of infection. Our technique may also address persistent pain due to over tensioning of the tenodesis or from lesions hidden within the groove, such as bicipital synovitis or partial tendon tears, that are not visualized in a standard open technique. We describe an all-arthroscopic onlay technique for biceps tendon fixation at an extra-articular position within the bicipital groove, above the pectoralis major insertion. The technique uses standard arthroscopic equipment and a single knotless suture anchor.

Consumers can receive guarantees through the certified evaluation tool of agriculture resources analysis of natural farming (CETARA NF) without having to deal with costly third-party certification's entry obstacles or complex participatory guarantee systems. The purpose of this study is to examine the reasons for farmers' engagement in CETARA-NF as well as their experiences and results. 400 farmers randomly interviewed from 4 agroclimatic zones using the well-structured questionnaire from a 135,000 certified farmers list. Most farmers fall under the Two Star category (61.5 %), followed by One Star (28.5 %) and Three Star (10 %). The findings imply that 61 per cent of farmers are transitioning towards natural farming as these farmers have minimised or eliminated the chemical use in their farming practices. Significant factors of adoption for this certification system were increased marketed quantity, chemical fertiliser cost and no certification cost. Also, market access (Rank 1), no cost of certification (Rank 2) and women friendly (Rank 3) recognized as the major advantages of this certification. CETARA-NF can be an alternative certification solution for smallholders by securing the integration of grassroots agroecological cooperation and customs inside natural farming systems, thereby overcoming certification barriers.

IntroductionEffective monitoring of insect-pests is vital for safeguarding agricultural yields and ensuring food security. Recent advances in computer vision and machine learning have opened up significant possibilities of automated persistent monitoring of insect-pests through reliable detection and counting of insects in setups such as yellow sticky traps. However, this task is fraught with complexities, encompassing challenges such as, laborious dataset annotation, recognizing small insect-pests in low-resolution or distant images, and the intricate variations across insect-pests life stages and species classes.MethodsTo tackle these obstacles, this work investigates combining two solutions, Hierarchical Transfer Learning (HTL) and Slicing-Aided Hyper Inference (SAHI), along with applying a detection model. HTL pioneers a multi-step knowledge transfer paradigm, harnessing intermediary in-domain datasets to facilitate model adaptation. Moreover, slicing-aided hyper inference subdivides images into overlapping patches, conducting independent object detection on each patch before merging outcomes for precise, comprehensive results.ResultsThe outcomes underscore the substantial improvement achievable in detection results by integrating a diverse and expansive in-domain dataset within the HTL method, complemented by the utilization of SAHI.DiscussionWe also present a hardware and software infrastructure for deploying such models for real-life applications. Our results can assist researchers and practitioners looking for solutions for insect-pest detection and quantification on yellow sticky traps.

Acromegaly due to ectopic secretion of growth hormone-releasing hormone (GHRH) is a rare disorder. The signs and symptoms of ectopic acromegaly are indistinguishable from acromegaly due to a somatotroph adenoma. A 35-year-old female presented with secondary amenorrhea for 10 years, intermittent headache, and reduced vision in both eyes for 4 years, which worsened over 4 months before presentation. Additionally, she was diagnosed with uncontrolled diabetes mellitus. On examination, she had coarse facial features, a fleshy nose, and acral enlargement. She had diminished visual acuity (left>right) and bitemporal hemianopia on perimetry. Biochemical investigations revealed elevated IGF-1 [588 ng/ml, reference range (RR) 100–242], markedly elevated basal growth hormone (>80 ng/ml; RR, 0.12–9.88), and hyperprolactinemia in the tumoral range (832 ng/ml; RR, 5–25). MRI sella demonstrated a 22×30×34mm sellar-suprasellar mass with T2 hypointensity. Chest imaging revealed a 75×87×106mm left lung mass, which was found to be a well-differentiated neuroendocrine tumor (NET) on biopsy. Plasma GHRH levels were elevated [38,088 ng/l; RR,

We examine the localized nature of COVID-19 pandemic spread in India. Through Gamma curve-fitting we observe that the infection patterns exhibit substantial variation across locations. Areas with larger male population and higher economic activity witness more cases. Economically-deprived districts experience higher mortality after controlling for the infections. Mobility in spatially contiguous locations is a significant determinant of new infections. Our study emphasizes the role of socioeconomic factors in explaining the variation across districts. The findings support the need for locally-specific policy, better medical infrastructure in socio-economically vulnerable localities and social-distancing measures in controlling the spread.

Generalisation across multiple tasks is a major challenge in deep learning for medical imaging applications, as it can cause a catastrophic forgetting problem. One commonly adopted approach to address these challenges is to train the model from scratch, incorporating old and new data, classes, and tasks. However, this solution comes with its downsides, as it is time-consuming, requires high computational resources, is susceptible to bias, and lacks flexibility. To effectively address these issues, this paper introduces a generalisable DL framework that consists of three key components: self-supervised learning, feature fusion of a single task, and feature fusion of new classes or tasks. Using the proposed framework, DL models with the SVM classifier can accurately detect abnormalities in X-ray tasks, including the humerus and wrist, achieving an accuracy of 92.71% and 90.74%, respectively. These results were achieved using a single classifier with minimal training requirements when new tasks were introduced. Another experiment was performed on chest X-rays, where new classes were added to the pre-existing ones. Without requiring retraining with both old and new classes, our framework achieved a combined class accuracy of 98.18%. This demonstrates that the model has not forgotten the old data. The proposed framework enhances performance and brings flexibility and efficiency to the training process, saving time and computational resources.

To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture experts including farmers are needed. Rapid advances in information and communication technology, precision agriculture and data analytics, are creating a perfect opportunity for the creation of smart connected farms (SCFs) and networked farmers. A network and coordinated farmer network provides unique advantages to farmers to enhance farm production and profitability, while tackling adverse climate events. The aim of this article is to provide a comprehensive overview of the state of the art in SCF including the advances in engineering, computer sciences, data sciences, social sciences and economics including data privacy, sharing and technology adoption. More specifically, we provide a comprehensive review of key components of SCFs and crucial elements necessary for its success. It includes, high-speed connections, sensors for data collection, and edge, fog and cloud computing along with innovative wireless technologies to enable cyber agricultural system. We also cover the topic of adoption of these technologies that involves important considerations around data analysis, privacy, and the sharing of data on platforms. From a social science and economics perspective, we examine the net-benefits and potential barriers to data-sharing within agricultural communities, and the behavioral factors influencing the adoption of SCF technologies. The focus of this review is to cover the state-of-the-art in smart connected farms with sufficient technological infrastructure; however, the information included herein can be utilized in geographies and farming systems that are witnessing digital technologies and want to develop SCF. Overall, taking a holistic view that spans technical, social and economic dimensions is key to understanding the impacts and future trajectory of Smart and Connected Farms.

Abstract Background Reverse Shoulder Arthroplasties (RSA) have become a primary choice for improving shoulder function and pain. However, the biomechanical failure mechanism of the humeral component is still unclear. The present study reports a novel protocol for microstructural imaging of the entire humerus implant under load before and after fracture. Methods A humerus specimen was obtained from a 75-year-old male donor. An expert surgeon implanted the specimen with a commonly used RSA implant (Aequalis reversed II, Stryker Orthopaedics, USA) and surgical procedure. The physiological glenohumeral contact force that maximized the distal implant migration was selected from a public repository ( orthoload.com ). Imaging and concomitant mechanical testing were performed using a large-volume micro-CT scanner (Nikon XT H 225 ST) and a custom-made compressive stage. Both when intact and once implanted, the specimen was tested under a pre-load and by imposing a constant deformation causing a physiological reaction load (650 N, 10 degrees adducted). The deformation of the implanted specimen was then increased up to fracture, which was identified by a sudden drop of the reaction force, and the specimen was then re-scanned. Results The specimen’s stiffness decreased from 874 N/mm to 464 N/mm after implantation, producing movements of the bone-implant interface consistent with the implant’s long-term stability reported in the literature. The micro-CT images displayed fracture of the tuberosity, caused by a combined compression and circumferential tension, induced by the distal migration of the implant. Conclusion The developed protocol offers detailed information on implant mechanics under load relative to intact conditions and fracture, providing insights into the failure mechanics of RSA implants. This protocol can be used to inform future implant design and surgical technique improvements.

Adversarial attacks pose a significant threat to deep learning models, specifically medical images, as they can mislead models into making inaccurate predictions by introducing subtle distortions to the input data that are often imperceptible to humans. Although adversarial training is a common technique used to mitigate these attacks on medical images, it lacks the flexibility to address new attack methods and effectively improve feature representation. This paper introduces a novel Model Ensemble Feature Fusion (MEFF) designed to combat adversarial attacks in medical image applications. The proposed model employs feature fusion by combining features extracted from different DL models and then trains Machine Learning classifiers using the fused features. It uses a concatenation method to merge the extracted features, forming a more comprehensive representation and enhancing the model's ability to classify classes accurately. Our experimental study has performed a comprehensive evaluation of MEFF, considering several challenging scenarios, including 2D and 3D images, greyscale and colour images, binary classification, and multi-label classification. The reported results demonstrate the robustness of using MEFF against different types of adversarial attacks across six distinct medical image applications. A key advantage of MEFF is its capability to incorporate a wide range of adversarial attacks without the need to train from scratch. Therefore, it contributes to developing a more diverse and robust defence strategy. More importantly, by leveraging feature fusion and ensemble modelling, MEFF enhances the resilience of DL models in the face of adversarial attacks, paving the way for improved robustness and reliability in medical image analysis.

Diagnostic delay leads to poor outcomes in infections, and it occurs more often when the causative agent is unusual. Delays are attributable to failing to consider such diagnoses in a timely fashion. Using routinely collected electronic health record (EHR) data, we built a preliminary multivariable diagnostic model for early identification of unusual fungal infections and tuberculosis in hospitalized patients. We conducted a two-gate case-control study. Cases encompassed adult patients admitted to 19 Mayo Clinic enterprise hospitals between January 2010 and March 2023 diagnosed with blastomycosis, cryptococcosis, histoplasmosis, mucormycosis, pneumocystosis, or tuberculosis. Control groups were drawn from all admitted patients (random controls) and those with community-acquired infections (ID-controls). Development and validation datasets were created using randomization for dividing cases and controls (7:3), with a secondary validation using ID-controls. A logistic regression model was constructed using baseline and laboratory variables, with the unusual infections of interest outcome. The derivation dataset comprised 1043 cases and 7000 random controls, while the 451 cases were compared to 3000 random controls and 1990 ID-controls for validation. Within the derivation dataset, the model achieved an area under the curve (AUC) of 0.88 (95% confidence interval [CI]: 0.87-0.89) with a good calibration accuracy (Hosmer-Lemeshow P = 0.623). Comparable performance was observed in the primary (AUC = 0.88; 95% CI: 0.86-0.9) and secondary validation datasets (AUC = 0.84; 95% CI: 0.82-0.86). In this multicenter study, an EHR-based preliminary diagnostic model accurately identified five unusual fungal infections and tuberculosis in hospitalized patients. With further validation, this model could help decrease time to diagnosis.

Abstract Background The shape and microstructure of the human crystalline lens alter with ageing, and this has an effect on the optical properties of the eye. The aim of this study was to characterise the age-related differences in the morphology and transparency of the eye lenses of healthy subjects through the optical signal discontinuity (OSD) zones in optical coherence tomography (OCT) images. We also investigated the association of those changes with the optical quality of the eye and visual function. Methods OCT images of the anterior segment of 49 eyes of subjects (9–78 years) were acquired, and the OSD zones (nucleus, C1–C4 cortical zones) were identified. Central thickness, curvature and optical density were measured. The eye’s optical quality was evaluated by the objective scatter index (OSI). Contrast sensitivity and visual acuity tests were performed. The correlation between extracted parameters and age was assessed. Results The increase in lens thickness with age was dominated by the thickening of the cortical zone C3 (0.0146 mm/year). The curvature radii of the anterior lens surface and both anterior and posterior nucleo-cortical interfaces decreased with age (− 0.053 mm/year, − 0.013 mm/year and − 0.006 mm/year, respectively), and no change was observed for the posterior lens radius. OCT-based densitometry revealed significant correlations with age for all zones except for C1β, and the highest increase in density was in the C2–C4 zones (R = 0.45, 0.74, 0.56, respectively, P

This meta-analysis review undertakes a comprehensive examination of various approaches for identifying myopathic fillets and meticulously evaluates the effects of bird age, deboning time, and different cooking and storage conditions on woody breast (WB) myopathic conditions in broiler deboned fillets. The data, meticulously collected from 20 articles based on predefined inclusion criteria sourced from various databases and online resources, reveal significant insights. For instance, the analysis uncovers that deboning time significantly affects Meullenet-Owens Razor Shear (MORS), Blunt Meullenet-Owens Razor Shear (BMORS), and descriptive analysis values (p < 0.001). Instrumentation techniques, such as compression force and shear force, along with different cooking conditions, strongly impact BMORS shear force values (R2 = 86.80%), with significance levels ranging from 0.01 to 0.001. Deboning time also substantially impacts MORS shear force values (R = 64.03%). In contrast, the effects of deboning time, bird age, and cooking conditions on descriptive sensory evaluation are minimal when compared to woody breast fillets (age of birds: R2 = 26.53%; cooking conditions: R2 = 32.57%; deboning time: R2 = 10.06%). The overall effect of bird age on chicken breast meat quality shows significant differences for the evaluated parameters (Hedges’ g [95% CI] = −0.72 [0.17, 1.26], I2 = 93%, p < 0.01). The sous vide cooking method significantly affects shear force energies and sensory descriptive evaluation for woody breast fillets (Hedges’ g [95% CI] = 5.30 [−50.30, 83.40], I2 = 98%, p < 0.01). These findings, with their significant implications, provide valuable insights for optimizing processing conditions in the poultry industry to reduce woody breast occurrences and enhance meat quality, instilling confidence in the robustness of the research.

In this paper, a low-profile, high-gain filtering antenna is proposed and developed for n-77 (3300–4200 MHz) and n-78 band (3300−3800 MHz) applications. The suggested filtering antenna is derived from a circular patch antenna. To efficiently reduce the harmonics, in the lower and upper stopbands, a pair of sectoral arcs and rectangular slots is etched out from the driven circular patch. Furthermore, the resultant structure is surrounded by a ring split at three different locations. Simulation studies reveal that this ring with three sectors is coupled with a driven patch and is highly responsible for extending the impedance bandwidth in the passband. This coupling also contributes to an extranull in the upper stopband region. In order to validate the simulated results, a prototype is fabricated, tested, and found that the measured results are consistent with the simulated one. The suggested filtering antenna, with a low profile of 0.0194λ0, demonstrates a wide fractional bandwidth of 10.2% ranging from 3.52 to 3.90 GHz, centred at 3.7 GHz. The maximum peak gain of 7.68 dB is achieved within the passband. Three configurable radiation nulls, first at 3.36 GHz, near the lower band edge; second at 4.04 GHz; and third at 4.24 GHz, near the upper band-edge frequencies, are generated to achieve fine out-of-band suppression levels.

Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results.

Background: Restoration of the glenoid joint line in shoulder arthroplasty is important for implant positioning and function. Medialization of the glenohumeral joint line due to glenoid bone loss is commonly encountered in primary and revision of shoulder arthroplasty albeit the direction and location of bone loss varies with different pathology. Three-Dimensional (3D) planning software has assisted in preoperative planning of complex glenoid deformities. However, limited literature is available defining a reliable 3D method to evaluate the glenoid joint line preoperatively. Aims: The purpose of this study is to identify a set of reliable scapular landmarks to be used as reference points to measure the premorbid glenoid joint line using 3D segmented models of healthy scapulae. Methods: Bilateral computed tomography scans from 79 patients eligible for primary stabilization procedures were retrospectively selected from our institutional surgical database (mean age 35 ± 10 years, 58 males and 21 females). 3D models of the contralateral healthy scapulae were created via computed tomography scan segmentation using Mimics 24.0 software (Materialise, Leuven, Belgium). Anatomical landmarks were identified using 3-Matic 16.0 software (Materialise, Leuven, Belgium). The distance between identified landmarks and a sagittal plane created on the deepest point of the glenoid was recorded for each scapula and reliability of each landmark was assessed. Inter- and intra-observer reliabilities were also evaluated using intraclass correlation coefficients (ICCs). Results: Four landmarks showed statistically significant results: the scapular notch (SN), the centroid of the coracoid (CC), a point on the most medial border of the scapula in line with the scapular spine (TS), and the most lateral point of the acromion (AL). The mean (± standard deviation) joint line measured from the SN, CC, TS and AL were 28.36 ± 2.97 mm, 11.66 ± 2.07 mm, 107.52 ± 8.1 mm, and 29.72 ± 4.46 mm, respectively. Inter-observer reliability analysis for SN, TS, and AL showed excellent agreement with ICC values of 0.966, 0.997, and 0.944, respectively, and moderate agreement for CC with ICC of 0.728. Conclusion: The results from this study assist in estimating joint line medialization preoperatively and in planning its subsequent restoration. A set of reliable landmarks can be used as references to estimate the premorbid glenoid joint line preoperatively.

Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.

Background: The study is aimed to investigate the metabolic alterations and changes in biochemical parameters associated with extended mask. Methods: It was a prospective comparative study conducted on 129 participants comprised of 37 healthy controls and 92 health care workers using different kind of masks like, cloth mask, surgical masks and N95-FFR/PPE. Two samples on day-1 and day-10 were collected for analysis of blood gas parameters, serum hypoxia-inducible factor-α (HIF-α), and erythropoietin (EPO). Results: Oxygen saturation percentage (sO2) of 72.68 (P = 0.033) was significantly low, whereas, Na+ (P = 0.05) and Ca2+ (P < 0.001) were raised in exposed individuals than the healthy controls. The serum HIF-α level of 3.26 ng/mL, was considerable higher in the exposed individuals than controls (P = 0.001). pO2 and sO2 were the lowest and HIF-α and EPO were raised in N95-FFR/PPE of all mask users (P < 0.01). A significant difference was evidenced for pCO2, pH, Na+, Ca2+, and EPO in the exposed group. A positive correlation between the duration of mask use (in hours) with HIF-α (r = 0.247, P = 0.005) and Ca2+ (r = 0.306, P < 0.001) was observed. The major complaints in N95-FFR/PPE users were headache (15.2%) and polydipsia (33.3%). Conclusion: The study findings depicted a significant metabolic alterations in PPE/N95 users which could be due to chronic hypoxic exposure of the tissues.

Hirayama disease (HD) is a rare, monomelic amyotrophy described by Hirayama in 1959. The condition is caused by chronic ischemic changes to the anterior horn cells of the cervical spine, caused by posterior dural sac laxity. It is a nonprogressive and self-limiting disease and has been noted to be a source of significant disability for affected young individuals. Early surgical intervention in HD limits the progression and thus decreases the degree of disability.