Purpose: This study was performed to assess the performance and accuracy of artificial intelligence (AI) in the detection and diagnosis of maxillary sinus pathologies using computed tomography (CT)/cone-beam computed tomography (CBCT) imaging. Materials and Methods: A comprehensive literature search was conducted across 4 databases: Google Scholar, BioMed Central (BMC), ProQuest, and PubMed. Combinations of keywords such as “DCNN,” “deep learning,” “convolutional neural network,” “machine learning,” “predictive modeling,” and “data mining” were used to identify relevant articles. The study included articles that were published within the last 5 years, written in English, available in full text, and focused on diagnostic accuracy. Results: Of an initial 530 records, 12 studies with a total of 3,349 patients (7,358 images) were included. All articles employed deep learning methods. The most commonly tested pathologies were maxillary rhinosinusitis and maxillary sinusitis, while the most frequently used AI models were convolutional neural network architectures, including ResNet and DenseNet, YOLO, and U-Net. DenseNet and ResNet architectures have demonstrated superior precision in detecting maxillary sinus pathologies due to their capacity to handle deeper networks without overfitting. The performance in detecting maxillary sinus pathology varied, with an accuracy ranging from 85% to 97%, a sensitivityof 87% to 100%, a specificity of 87.2% to 99.7%, and an area under the curve of 0.80 to 0.91. Conclusion AI with various architectures has been used to detect maxillary sinus abnormalities on CT/CBCT images, achieving near-perfect results. However, further improvements are needed to increase accuracy and consistency.

Purpose: This study was performed to assess the performance and accuracy of artificial intelligence (AI) in the detection and diagnosis of maxillary sinus pathologies using computed tomography (CT)/cone-beam computed tomography (CBCT) imaging. Materials and Methods: A comprehensive literature search was conducted across 4 databases: Google Scholar, BioMed Central (BMC), ProQuest, and PubMed. Combinations of keywords such as “DCNN,” “deep learning,” “convolutional neural network,” “machine learning,” “predictive modeling,” and “data mining” were used to identify relevant articles. The study included articles that were published within the last 5 years, written in English, available in full text, and focused on diagnostic accuracy. Results: Of an initial 530 records, 12 studies with a total of 3,349 patients (7,358 images) were included. All articles employed deep learning methods. The most commonly tested pathologies were maxillary rhinosinusitis and maxillary sinusitis, while the most frequently used AI models were convolutional neural network architectures, including ResNet and DenseNet, YOLO, and U-Net. DenseNet and ResNet architectures have demonstrated superior precision in detecting maxillary sinus pathologies due to their capacity to handle deeper networks without overfitting. The performance in detecting maxillary sinus pathology varied, with an accuracy ranging from 85% to 97%, a sensitivityof 87% to 100%, a specificity of 87.2% to 99.7%, and an area under the curve of 0.80 to 0.91. Conclusion AI with various architectures has been used to detect maxillary sinus abnormalities on CT/CBCT images, achieving near-perfect results. However, further improvements are needed to increase accuracy and consistency.

Purpose: This study was performed to assess the performance and accuracy of artificial intelligence (AI) in the detection and diagnosis of maxillary sinus pathologies using computed tomography (CT)/cone-beam computed tomography (CBCT) imaging. Materials and Methods: A comprehensive literature search was conducted across 4 databases: Google Scholar, BioMed Central (BMC), ProQuest, and PubMed. Combinations of keywords such as “DCNN,” “deep learning,” “convolutional neural network,” “machine learning,” “predictive modeling,” and “data mining” were used to identify relevant articles. The study included articles that were published within the last 5 years, written in English, available in full text, and focused on diagnostic accuracy. Results: Of an initial 530 records, 12 studies with a total of 3,349 patients (7,358 images) were included. All articles employed deep learning methods. The most commonly tested pathologies were maxillary rhinosinusitis and maxillary sinusitis, while the most frequently used AI models were convolutional neural network architectures, including ResNet and DenseNet, YOLO, and U-Net. DenseNet and ResNet architectures have demonstrated superior precision in detecting maxillary sinus pathologies due to their capacity to handle deeper networks without overfitting. The performance in detecting maxillary sinus pathology varied, with an accuracy ranging from 85% to 97%, a sensitivityof 87% to 100%, a specificity of 87.2% to 99.7%, and an area under the curve of 0.80 to 0.91. Conclusion AI with various architectures has been used to detect maxillary sinus abnormalities on CT/CBCT images, achieving near-perfect results. However, further improvements are needed to increase accuracy and consistency.

Introduction: To assess outcomes of FHL transfer and V-Y plasty for chronic Achilles rupture due to insertional Achilles tendinopathy. Materials and methods: A case series of 12 patients was conducted between 1st January 2017 and 31st December 2018. The patients had short flexor hallucis longus tendon transfer with gastrocnemius lengthening by V-Y plasty for Achilles tendon rupture. Patients were allowed full weight bearing at six weeks post-operatively, and were followed up at three months and six months post-operatively, when the range of motion of the ankle was examined, and the outcome was assessed using the EFAS score. Results: Of the 12 patients in the study, the majority were males; the mean age was 50.6±8.96 years. A significant improvement in dorsiflexion and plantarflexion was noted at the six-month follow-up compared to the three-month follow-up (P=<0.001 for both). When compared to the normal side, dorsiflexion and plantarflexion of the affected ankle were significantly less at three months but were comparable at six months post-operatively. A significant improvement was noted in the mean EFAS score at the sixmonth follow-up (25.5±5.71) compared to three months (18.6±0.90) post-surgery (P=0.001). Males were also noted to have significantly higher EFAS scores at their six-month follow-up than females (P=0.022). In contrast, a negative correlation was noted between the European Foot and Ankle Society (EFAS) score at the final follow-up and age (P=0.011). Conclusion: FHL tendon transfer with V-Y plasty in chronic Achilles rupture due to insertional Achilles tendinopathy is an effective procedure resulting in the restoration of the ankle range of motion and improvement in functional scores.

Objective: The Commonwealth of the Northern Mariana Islands (CNMI) is a remote Pacific island territory with a population of 47 329 that successfully prevented the significant introduction of coronavirus disease (COVID-19) until late 2021. This study documents how the response to the introduction of COVID-19 in CNMI in 2021 was conducted with limited resources without overwhelming local clinical capacity or compromising health service delivery for the population. Methods: Data from COVID-19 case investigations, contact tracing, the Commonwealth’s immunization registry and whole genome sequencing were collated and analysed as part of this study. Results: Between 26 March 2020 and 31 December 2021, 3281 cases and 14 deaths due to COVID-19 were reported in CNMI (case fatality rate, 0.4%). While notification rates were highest among younger age groups, hospitalization and mortality rates were disproportionately greater among those aged >50 years and among the unvaccinated. The first widespread community transmission in CNMI was detected in October 2021, with genomic epidemiology and contact tracing data indicating a single introduction event involving the AY.25 lineage and subsequent rapid community spread. Vaccination coverage was high before widespread transmission occurred in October 2021 and increased further over the study period. Discussion: Robust preparedness and strong leadership generated resilience within the public health sector such that COVID-19 did not overwhelm CNMI’s health system as it did in other jurisdictions and countries around the world. At no point was hospital capacity exceeded, and all patients received adequate care without the need for health-care rationing.

Plants are alternative source of natural medicines due to secondary active metabolites. Fagonia cretica extracts and Gradient High-Pressure Liquid Chromatography fractionations were checked against multidrug-resistant gastrointestinal pathogens including, Salmonella typhi, Escherichia coli and Shigella flexneri. ESI-MS/MS analysis of bioactive HPLC fractions was performed to elucidate antibacterial compounds. F. cretica extracts exhibited potential antibacterial activity. Twenty-four (24) HPLC fractions were obtained from methanol, ethanol and aqueous extracts of F. cretica. Eighteen (18) fractions showed antibacterial activity, while no activity was observed by the remaining six (6) fractions. HPLC fractions, F1 (25g ± 0.20 mm) and F2 (15f ± 0.12 mm) of aqueous extract exhibited activity against multidrug resistant GI pathogens. Gallic acid, quinic acid, cyclo-l-leu-l-pro, vidalenolone, liquirtigenin, rosmarinic acid and cerebronic acid were identified in F1 fraction of aqueous extract, while succinic acid, cyclo (l-Leul-Pro) and liquirtigenin were identified in F2 fraction of aqueous extract through ESI-MS/MS analysis. F. cretica extracts and HPLC fractions showed potential activity against MDR GI pathogens. Vidalenolone, Cyclo-1-leu-1-pro and Cerebronic acid are first time reported in F. cretica. Further characterization of bioactive compounds from F. cretica may be helpful to elucidate antibacterial therapeutic molecules.

COVID-19/virology* ; Cities ; Environmental Monitoring ; Genome, Viral ; Humans ; Infection Control/methods* ; Pakistan ; RNA, Viral/genetics* ; SARS-CoV-2/genetics* ; Sewage/virology*

The Division of Cancer Prevention of the National Cancer Institute (NCI) and the Office of Disease Prevention of the National Institutes of Health co-sponsored the Translational Advances in Cancer Prevention Agent Development Meeting on August 27 to 28, 2020. The goals of this meeting were to foster the exchange of ideas and stimulate new collaborative interactions among leading cancer prevention researchers from basic and clinical research; highlight new and emerging trends in immunoprevention and chemoprevention as well as new information from clinical trials; and provide information to the extramural research community on the significant resources available from the NCI to promote prevention agent development and rapid translation to clinical trials. The meeting included two plenary talks and five sessions covering the range from pre-clinical studies with chemo/immunopreventive agents to ongoing cancer prevention clinical trials. In addition, two NCI informational sessions describing contract resources for the preclinical agent development and cooperative grants for the Cancer Prevention Clinical Trials Network were also presented.

The Division of Cancer Prevention of the National Cancer Institute (NCI) and the Office of Disease Prevention of the National Institutes of Health co-sponsored the Translational Advances in Cancer Prevention Agent Development Meeting on August 27 to 28, 2020. The goals of this meeting were to foster the exchange of ideas and stimulate new collaborative interactions among leading cancer prevention researchers from basic and clinical research; highlight new and emerging trends in immunoprevention and chemoprevention as well as new information from clinical trials; and provide information to the extramural research community on the significant resources available from the NCI to promote prevention agent development and rapid translation to clinical trials. The meeting included two plenary talks and five sessions covering the range from pre-clinical studies with chemo/immunopreventive agents to ongoing cancer prevention clinical trials. In addition, two NCI informational sessions describing contract resources for the preclinical agent development and cooperative grants for the Cancer Prevention Clinical Trials Network were also presented.

Toxoplasma gondii is a protozoan parasite that can infect all mammals, serving as intermediate hosts. The cause of congenital toxoplasmosis is transplacental transmission of the parasite to the foetus, resulting in wide range of manifestations from mild chorioretinitis to miscarriage. Its frequency can be reduced by early screening of pregnant women which is based mainly on tests for anti-Toxoplasma antibodies. We collected serum samples of 594 pregnant women (subjects) after taking their consent over a period of two years (2016-2018) and analyzed them for anti-Toxoplasma IgG by ELISA. The positive samples were then analyzed for IgG avidity test which could differentiate between recent and past infections. The seroprevalence was also correlated with the age of the subjects and their contact with cats. 162 subjects were found positive out of which only three showed a recent infection. After following up until delivery, one of them delivered a baby who had jaundice and was diagnosed with anti-Toxoplasma IgM at birth. The foetus of the second subject died in-utero, while the third woman delivered a normal baby after being given spiramycin when diagnosed with toxoplasmosis in the first trimester. It was found that most of the positive subjects had frequent contact with cats. Invasion of the parasite during third trimester resulted in death in-utero and jaundice. Most common cause of pregnancy wastage during our study was spontaneous abortions while pregnancy loss due to congenital anomalies was rare.