Surgical management of neonatal severe hyperparathyroidism.

BACKGROUND: Neonatal severe hyperparathyroidism (NSHPT) is a rare disease that can be lethal. Most patients require parathyroidectomy. OBJECTIVE: Report experience in managing this severe disease. DESIGN: Retrospective chart review of case series. SETTING: Tertiary health care center. PATIENTS AND METHODS: We reviewed data on patients managed for NSHPT from June 2001 to January 2023. Demographic, clinical, and follow-up data were collected, and descriptive data were generated. MAIN OUTCOME MEASURES: Pre- and postoperative levels of parathyroid hormone (PTH) and serum calcium, and effect of autotransplantation. SAMPLE SIZE: 19. RESULTS: The 13 males and 6 females had a a mean age of 46 days at referral. The mean preoperative parathyroid hormone (PTH) and serum calcium levels were 996 ng/L and 4.54 mmol/L, respectively. Twelve patients underwent ultrasonography preoperatively. Of these, six had prominent glands, while no glands were seen in the other six. A Sestamibi scan was done for 15 patients, of which nine showed negative results and six showed positive results, with three glands observed in the neck and three in the sublingual area. Nineteen patients underwent renal ultrasonography, with nine showing nephrocalcinosis. The mean age at surgery was 5.2 months. Total parathyroidectomy (four glands) was performed in 17 patients, and 15 underwent concurrent auto-transplantation. One patient had three glands removed, in addition to auto-transplantation. Another underwent single gland excision as a redo-surgery after previous surgery elsewhere. The mean postoperative follow-up duration was 6 years. The mean postoperative PTH and calcium levels were 25 ng/L and 1.64 mmol/L, respectively. Ultimately, all the patients were required to initiate calcium and vitamin D supplements, except for two patients who had undergone auto-transplantation. Molecular genetic screening of the calcium-sensing receptor gene reported likely pathogenic/pathogenic mutations in 16 of 19 patients (13 were homozygous, two were heterozygous, one was negative, and data was unavailable for the remaining three patients). CONCLUSIONS: Surgical treatment of NSHPT is effective. Preoperative radiological localization studies did not impact the treatment plan. Auto-transplantation proved ineffective in maintaining independence from medical supplements. LIMITATIONS: The retrospective nature of the study may imply inaccuracybut since the data are gathered from electronic medical records, we believe it is highly accurate. The small sample size limits generalizability.

Toward the automated detection of behavioral changes associated with the post-weaning transition in pigs.

We modified an automated method capable of quantifying behaviors which we then applied to the changes associated with the post-weaning transition in pigs. The method is data-driven and depends solely on video-captured image data without relying on sensors or additional pig markings. It was applied to video images generated from an experiment during which post-weaned piglets were subjected to treatments either containing or not containing in-feed antimicrobials (ZnO or antibiotics). These treatments were expected to affect piglet performance and health in the short-term by minimizing the risk from post-weaning enteric disorders, such as diarrhea. The method quantified total group feeding and drinking behaviors as well as posture (i.e., standing and non-standing) during the first week post-weaning, when the risk of post-weaning diarrhea is at its highest, by learning from the variations within each behavior using data manually annotated by a behavioral scientist. Automatically quantified changes in behavior were consistent with the effects of the absence of antimicrobials on pig performance and health, and manifested as reduced feed efficiency and looser feces. In these piglets both drinking and standing behaviors were increased during the first 6 days post-weaning. The correlation between fecal consistency and drinking behavior 6 days post weaning was relatively high, suggesting that these behaviors may have a diagnostic value. The presence or absence of in-feed antimicrobials had no effect on feeding behavior, which, however, increased over time. The approach developed here is capable of automatically monitoring several different behaviors of a group of pigs at the same time, and potentially this may be where its value as a diagnostic tool may lie.

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs.

Changes in pig behaviours are a useful aid in detecting early signs of compromised health and welfare. In commercial settings, automatic detection of pig behaviours through visual imaging remains a challenge due to farm demanding conditions, e.g., occlusion of one pig from another. Here, two deep learning-based detector methods were developed to identify pig postures and drinking behaviours of group-housed pigs. We first tested the system ability to detect changes in these measures at group-level during routine management. We then demonstrated the ability of our automated methods to identify behaviours of individual animals with a mean average precision of [Formula: see text], under a variety of settings. When the pig feeding regime was disrupted, we automatically detected the expected deviations from the daily feeding routine in standing, lateral lying and drinking behaviours. These experiments demonstrate that the method is capable of robustly and accurately monitoring individual pig behaviours under commercial conditions, without the need for additional sensors or individual pig identification, hence providing a scalable technology to improve the health and well-being of farm animals. The method has the potential to transform how livestock are monitored and address issues in livestock farming, such as targeted treatment of individuals with medication.

Outcome of childhood lupus nephritis in Saudi children.

Our aim in this study is to report the long-term renal outcome of a cohort of Saudi children with systemic lupus erythematosus (SLE). All patients with childhood lupus nephritis (cLN) proved by renal biopsy seen between January 2000 and June 2015 were reviewed. The renal outcome was assessed according to serum creatinine level, protein/creatinine ratio at the last follow-up visit, and/or evidence of renal impairment during follow-up period and end-stage renal disease (ESRD). Additional outcome measures include accrual damage measured by pediatric adaptation of the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (pSDI), and death related to SLE was determined. A total of 84 (72 females) cLN patients with follow-up duration of 9.3 years (±5.2) were included in this study. The mean current age was 19.4 years (±5.5) and mean age at onset was 9.2 years (±2.4). The most frequent histopathological class was proliferative glomerulonephritis (64.3%) followed by membranous nephritis (27.4%). The mean activity and chronicity indices were 5.9 (±3.9) and 2.9 (±2.2), respectively. Renal microthrombosis was found in 9 (10.7%) patients. All patients treated with immunosuppressive medications; cyclophosphamide used in 64 followed by mycophenolate mofetil in 42, then azathioprine in 19 patients, while rituximab used in 24 patients. At the last follow-up visit, the mean serum creatinine was 147 umol/L (±197) and the mean protein/ creatinine ratio was 0.8 (± 1.1) while the mean total pSDI was 1.9 (±1.9) and mean renal SDI was 0.7 (±1.1). Sixteen (19%) patients had ESRD and eight of them had class IV nephritis. However, there was no significant difference in ESRD by histological class. The overall survival rates were five years: 94% and 10 years: 87%. Infection was the leading cause of mortality. Our patients had severe cLN and required intensive treatment. Despite the survival rate is comparable to other studies, ESRD is more frequent and this may be attributed to genetic or ethnic factors.

Deep learning-based artificial vision for grasp classification in myoelectric hands.

OBJECTIVE: Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable trans-radial amputees to use a simple, yet efficient, computer vision system to grasp and move common household objects with a two-channel myoelectric prosthetic hand. APPROACH: We developed a deep learning-based artificial vision system to augment the grasp functionality of a commercial prosthesis. Our main conceptual novelty is that we classify objects with regards to the grasp pattern without explicitly identifying them or measuring their dimensions. A convolutional neural network (CNN) structure was trained with images of over 500 graspable objects. For each object, 72 images, at [Formula: see text] intervals, were available. Objects were categorised into four grasp classes, namely: pinch, tripod, palmar wrist neutral and palmar wrist pronated. The CNN setting was first tuned and tested offline and then in realtime with objects or object views that were not included in the training set. MAIN RESULTS: The classification accuracy in the offline tests reached [Formula: see text] for the seen and [Formula: see text] for the novel objects; reflecting the generalisability of grasp classification. We then implemented the proposed framework in realtime on a standard laptop computer and achieved an overall score of [Formula: see text] in classifying a set of novel as well as seen but randomly-rotated objects. Finally, the system was tested with two trans-radial amputee volunteers controlling an i-limb UltraTM prosthetic hand and a motion controlTM prosthetic wrist; augmented with a webcam. After training, subjects successfully picked up and moved the target objects with an overall success of up to [Formula: see text]. In addition, we show that with training, subjects' performance improved in terms of time required to accomplish a block of 24 trials despite a decreasing level of visual feedback. SIGNIFICANCE: The proposed design constitutes a substantial conceptual improvement for the control of multi-functional prosthetic hands. We show for the first time that deep-learning based computer vision systems can enhance the grip functionality of myoelectric hands considerably.