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Validation of your direct-to-PCR COVID-19 discovery protocol utilizing mechanised homogenization: A single regarding decreasing assets necessary for accurate tests.
Objective Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing disease (CD). Methods All consecutive patients in our center who underwent surgery for CD through the endoscopic endonasal approach were retrospectively reviewed. Study endpoints were gross-tumor removal (GTR), postsurgical remission, and long-term control of disease. Several demographic, radiological, and histological factors were assessed as potential predictors. For ML-based modeling, data were randomly divided into 2 sets with an 80% to 20% ratio for bootstrapped training and testing, respectively. Several algorithms were tested and tuned for the area under the curve (AUC). Results The study included 151 patients. GTR was achieved in 137 patients (91%), and postsurgical hypersecretion remission was achieved in 133 patients (88%). Eflornithine molecular weight At last follow-up, 116 patients (77%) were still in remission after surgery and in 21 patients (14%), CD was controlled with complementary treatment (overall, of 131 cases, 87% were under control at follow-up). At internal validation, the endpoints were predicted with AUCs of 0.81-1.00, accuracy of 81%-100%, and Brier scores of 0.035-0.151. Tumor size and invasiveness and histological confirmation of adrenocorticotropic hormone (ACTH)-secreting cells were the main predictors for the 3 endpoints of interest. Conclusions ML algorithms were used to train and internally validate robust models for all the endpoints, giving accurate outcome predictions in CD cases. This analytical method seems promising for potentially improving future patient care and counseling; however, careful clinical interpretation of the results remains necessary before any clinical adoption of ML. Moreover, further studies and increased sample sizes are definitely required before the widespread adoption of ML to the study of CD.Objective The purpose of this study was to analyze the clinical and biochemical outcome of consecutive patients with acromegaly after microscopic transsphenoidal surgery (MTS) at a single center over an 8-year period. Methods A retrospective analysis of patients with acromegaly treated via MTS between 2008 and 2015 at the authors' center was performed. The mean follow-up was 29 months (range 1-120 months). Parameters investigated included tumor size, pre- and postoperative insulin-like growth factor-I, growth hormone levels, pretreatment, perioperative complications, and clinical outcome. Results A total of 280 patients with acromegaly were treated surgically at the authors' center over the abovementioned time frame and were included in analyses. For 231 of these patients, complete follow-up data were available for evaluation. One hundred eighty-eight patients (81%) showed remission initially according to current criteria. So far, 23 of these patients relapsed in the further course, so that on follow-up 165 patients (71%) demonstrated full remission by surgery alone. Most patients in whom remission after surgery failed were treated with somatostatin receptor ligands and/or dopamine agonists as second-line treatment. The main postoperative complications included transient hyponatremia and diabetes insipidus (13/280; 4.6%). CSF leakage only occurred in 2 cases (2/280; 0.7%). No surgery-related death occurred. Conclusions The data underline the effectiveness of MTS in acromegaly. Many patients with recurrent disease or incomplete tumor resection can be successfully managed pharmacologically.Background Most of the general public often has never met a living kidney donor, let alone considered if they would ever donate a kidney themselves while they are alive. Narrative storytelling, the sharing of first-person narratives based on lived experience, may be an important way to improve education about living donor kidney transplant (LDKT). Developing ways to easily standardize and disseminate diverse living donor stories using digital technology could inspire more people to consider becoming living donors and reduce the kidney shortage nationally. Objective This manuscript describes the development of the Living Donation Storytelling Project, an online digital library of living donation narratives from multiple audiences using video capture technology. Specifically, we describe the theoretical foundation and development of the library, a protocol to capture diverse storytellers, the characteristics and experiences of storytellers participating, and the frequency with which any ethical concerns about cgth of ten minutes (0046 sec - 3216 min). Ninety-five percent (130 out of 137), were motivated by a desire to educate the public, 107 (78%) were motivated to help more people become living donors, and 104 (76%), were motivated to dispel myths. Ease of using the technology and telling their story varied, with fear of being on film, emotional difficulty talking about their experiences, and some technological barriers being reported. Protected Health Information, most commonly surnames and transplant center names, was present in 63% of stories and was edited out. Conclusions With appropriate sensitivity to ensure diverse recruitment, ethical review of content, and support for storytellers, online storytelling platforms may be a cost-effective, convenient way to further engage patients and increase the curiosity of the public in learning more about the possibility of becoming living donors.Background Recent advancement of wearable sensor technology has shown feasibility of remote physical therapy at home. Especially, current crisis of pandemic has revealed the need and opportunity of internet-based wearable technology in the future healthcare system. Previous researches have shown the feasibility of human activity recognition technology for monitoring rehabilitation activities at home environments; however, few comprehensive studies ranging from the development to the clinical evaluation exist. Objective This study aims to 1) develop a home-based rehabilitation (HBR) system which can recognize and record type and frequency of rehabilitation exercises conducted by the user by using smartwatch, smartphone application equipped with machine learning (ML) algorithm, and to 2) evaluate the efficacy of the home-based rehabilitation system through prospective comparative study with chronic stroke survivors. Methods The HBR system consisted of off-the-shelf smartwatch, smartphone, and custom developed applications.
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