Matrix-induced autologous chondrocyte implantation (MACI) is an established technique used to treat osteochondral lesions in the knee. For larger osteochondral lesions (> 5 cm²) deeper than approximately 8 mm we have combined the use of two MACI membranes with impaction grafting of the subchondral bone. We report our results of 14 patients who underwent the ‘bilayer collagen membrane’ technique (BCMT) with a mean follow-up of 5.2 years (2 to 8). There were 12 men and two women with a mean age of 23.6 years (16 to 40). The mean size of the defect was 7.2 cm²(5.2 to 12 cm²) and were located on the medial (ten) or lateral (four) femoral condyles. The mean modified Cincinnati knee score improved from 45.1 (22 to 70) pre-operatively to 82.8 (34 to 98) at the most recent review (p < 0.05). The visual analogue pain score improved from 7.3 (4 to 10) to 1.7 (0 to 6) (p < 0.05). Twelve patients were considered to have a good or excellent clinical outcome. One graft failed at six years.The BCMT resulted in excellent functional results and durable repair of large and deep osteochondral lesions without a high incidence of graft-related complications.

Efficient bed management minimizes hospital costs and improves efficiency and patient outcomes. This study presents a predictive hospital-ICU length of stay (LOS) framework at admission, where it leverages hospital EHR. Our work utilizes supervised machine learning classification models to predict ICU patients’ LOS in hospital clinical information systems (CIS). Our research marks the first known instance of utilizing explainable artificial intelligence (xAI) for the purpose of explainable machine learning applied to real data collected from hospital stays. We evaluated the predictive classification models using a range of performance metrics (Accuracy, AUC, Sensitivity, Specificity, F1- score, Precision, Recall and more) to predict short and long ICU lengths of stay upon hospital admission. XGBoost predicted short and long LOS with a 98 % AUC. This study shows how hospitals and ICUs might use machine learning to forecast patients on admission. Our study extends clinical information systems for hospitals to provide robust and trustworthy LOS, predictive models by using xAI to explain predictive model outputs.

COVID-19 has posed an unprecedented global public health threat and caused a significant number of severe cases that necessitated long hospitalization and overwhelmed health services in the most affected countries. In response, governments initiated a series of non-pharmaceutical interventions (NPIs) that led to severe economic and social impacts. The effect of these intervention measures on the spread of the COVID-19 pandemic are not well investigated within developing country settings. This study simulated the trajectories of the COVID-19 pandemic curve in Jordan between February and May and assessed the effect of Jordan’s strict NPI measures on the spread of COVID-19. A modified susceptible, exposed, infected, and recovered (SEIR) epidemic model was utilized. The compartments in the proposed model categorized the Jordanian population into six deterministic compartments: suspected, exposed, infectious pre-symptomatic, infectious with mild symptoms, infectious with moderate to severe symptoms, and recovered. The GLEAMviz client simulator was used to run the simulation model. Epidemic curves were plotted for estimated COVID-19 cases in the simulation model, and compared against the reported cases. The simulation model estimated the highest number of total daily new COVID-19 cases, in the pre-symptomatic compartmental state, to be 65 cases, with an epidemic curve growing to its peak in 49 days and terminating in a duration of 83 days, and a total simulated cumulative case count of 1048 cases. The curve representing the number of actual reported cases in Jordan showed a good pattern compatibility to that in the mild and moderate to severe compartmental states. The reproduction number under the NPIs was reduced from 5.6 to less than one. NPIs in Jordan seem to be effective in controlling the COVID-19 epidemic and reducing the reproduction rate. Early strict intervention measures showed evidence of containing and suppressing the disease.

BACKGROUND:Immunoglobulin G4 (IgG4)-related disease is immune-mediated and was first proposed as a defined entity after studies on patients with autoimmune pancreatitis. Since then, it has been reported in many organs. Involvement of the ovaries is rare, and to our knowledge, only 2 cases have been reported in the literature. IgG4-related disease is associated with increased serum IgG4 levels. Organ involvement includes a lymphoplasmacytic infiltrate, fibrosis, and obliterative phlebitis, with immunohistochemistry showing IgG4-positive plasma cells. This report is of a case of IgG4-related disease involving the right ovary. CASE REPORT:A 47-year-old woman presented with a right ovarian cyst. An ultrasound scan revealed a complex right ovarian cyst with multiple septations. The hormonal profile and tumor markers were unremarkable. Gross examination showed fragments of cyst wall. Histologic examination revealed a follicular cyst, surrounded by a dense lymphoplasmacytic infiltrate rich in eosinophils, partially obliterative phlebitis, and fibrosis. Immunohistochemically, IgG marked most of the plasma cells, of which 70% expressed IgG4, with a count >50 cells per high-power field. Subsequent testing of serum IgG4 showed that the level was elevated (330 mg/dL). A diagnosis of IgG4-related disease was made. CONCLUSIONS:Ovarian involvement by IgG4-related disease is rarely described in the literature. Our patient is likely to be the third case. We believe that cumulative findings from our case along with the 2 already reported cases increase awareness and may establish a framework for building more objective criteria to define this entity in the ovaries, similar to what has been achieved in some other organs.