Engineering of Escherichia coli to facilitate efficient utilization of isomaltose and panose in industrial glucose feedstock.

Industrial glucose feedstock prepared by enzymatic digestion of starch typically contains significant amounts of disaccharides such as maltose and isomaltose and trisaccharides such as maltotriose and panose. Maltose and maltosaccharides can be utilized in Escherichia coli fermentation using industrial glucose feedstock because there is an intrinsic assimilation pathway for these sugars. However, saccharides that contain α-1,6 bonds, such as isomaltose and panose, are still present after fermentation because there is no metabolic pathway for these sugars. To facilitate more efficient utilization of glucose feedstock, we introduced glvA, which encodes phospho-α-glucosidase, and glvC, which encodes a subunit of the phosphoenolpyruvate-dependent maltose phosphotransferase system (PTS) of Bacillus subtilis, into E. coli. The heterologous expression of glvA and glvC conferred upon the recombinant the ability to assimilate isomaltose and panose. The recombinant E. coli assimilated not only other disaccharides but also trisaccharides, including alcohol forms of these saccharides, such as isomaltitol. To the best of our knowledge, this is the first report to show the involvement of the microbial PTS in the assimilation of trisaccharides. Furthermore, we demonstrated that an L-lysine-producing E. coli harboring glvA and glvC converted isomaltose and panose to L-lysine efficiently. These findings are expected to be beneficial for industrial fermentation.

Development of artificial intelligence-based slow-motion echocardiography and clinical usefulness for evaluating regional wall motion abnormalities.

The diagnostic accuracy of exercise stress echocardiography (ESE) for myocardial ischemia requires improvement, given that it currently depends on the physicians' experience and image quality. To address this issue, we aimed to develop artificial intelligence (AI)-based slow-motion echocardiography using inter-image interpolation. The clinical usefulness of this method was evaluated for detecting regional wall-motion abnormalities (RWMAs). In this study, an AI-based echocardiographic image-interpolation pipeline was developed using optical flow calculation and prediction for in-between images. The accuracy for detecting RWMAs and image readability among 25 patients with RWMA and 25 healthy volunteers was compared between four cardiologists using slow-motion and conventional ESE. Slow-motion echocardiography was successfully developed for arbitrary time-steps (e.g., 0.125×, and 0.5×) using 1,334 videos. The RWMA detection accuracy showed a numerical improvement, but it was not statistically significant (87.5% in slow-motion echocardiography vs. 81.0% in conventional ESE; odds ratio: 1.43 [95% CI: 0.78-2.62], p = 0.25). Interreader agreement analysis (Fleiss's Kappa) for detecting RWMAs among the four cardiologists were 0.66 (95%CI: 0.55-0.77) for slow-motion ESE and 0.53 (95%CI: 0.42-0.65) for conventional ESE. Additionally, subjective evaluations of image readability using a four-point scale showed a significant improvement for slow-motion echocardiography (2.11 ± 0.73 vs. 1.70 ± 0.78, p < 0.001).In conclusion, we successfully developed slow-motion echocardiography using in-between echocardiographic image interpolation. Although the accuracy for detecting RWMAs did not show a significant improvement with this method, we observed enhanced image readability and interreader agreement. This AI-based approach holds promise in supporting physicians' evaluations.

Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers' performance and final diagnosis.

PURPOSE: To evaluate the performance of a deep learning-based computer-aided detection (CAD) software for detecting pulmonary nodules, masses, and consolidation on chest radiographs (CRs) and to examine the effect of readers' experience and data characteristics on the sensitivity and final diagnosis. MATERIALS AND METHODS: The CRs of 453 patients were retrospectively selected from two institutions. Among these CRs, 60 images with abnormal findings (pulmonary nodules, masses, and consolidation) and 140 without abnormal findings were randomly selected for sequential observer-performance testing. In the test, 12 readers (three radiologists, three pulmonologists, three non-pulmonology physicians, and three junior residents) interpreted 200 images with and without CAD, and the findings were compared. Weighted alternative free-response receiver operating characteristic (wAFROC) figure of merit (FOM) was used to analyze observer performance. The lesions that readers initially missed but CAD detected were stratified by anatomic location and degree of subtlety, and the adoption rate was calculated. Fisher's exact test was used for comparison. RESULTS: The mean wAFROC FOM score of the 12 readers significantly improved from 0.746 to 0.810 with software assistance (P = 0.007). In the reader group with < 6 years of experience, the mean FOM score significantly improved from 0.680 to 0.779 (P = 0.011), while that in the reader group with ≥ 6 years of experience increased from 0.811 to 0.841 (P = 0.12). The sensitivity of the CAD software and the adoption rate for the lesions with subtlety level 2 or 3 (obscure) lesions were significantly lower than for level 4 or 5 (distinct) lesions (50% vs. 93%, P < 0.001; and 55% vs. 74%, P = 0.04, respectively). CONCLUSION: CAD software use improved doctors' performance in detecting nodules/masses and consolidation on CRs, particularly for non-expert doctors, by preventing doctors from missing distinct lesions rather than helping them to detect obscure lesions.

Clinical utility of the Bosniak classification version 2019: Diagnostic value of adding magnetic resonance imaging to computed tomography examination.

PURPOSE: To assess the impact of the updated Bosniak classification (BC2019) for cystic renal masses (CRMs) on interobserver agreement between radiologists and urologists and the diagnostic value of adding MRI to CT examination (combined CT/MRI). METHOD: This study included 103 CRMs from 83 consecutive patients assessed using contrast-enhanced CT and MRI between 2010 and 2016. Nine readers in three groups (three radiologists, three radiology residents, and three urologists) reviewed CT alone and the combined CT/MRI using BC2019. Bosniak category was determined by consensus in each group for diagnosing malignancy, with a cut-off category of ≧III. Interobserver agreement was assessed using Fleiss' kappa values. The effect of CT or combined CT/MRI on the diagnosis of malignancy was assessed using McNemar's test. RESULTS: Interobserver agreement of BC2019 for CT alone was substantial for radiologists and residents, moderate for urologists (0.77, 0.63, and 0.58, respectively). Interobserver agreement of BC2019 for combined CT/MRI was substantial for all three groups (radiologists: 0.78; residents: 0.65; and urologists: 0.61). Among residents, the sensitivity/specificity/accuracy rates of combined CT/MRI vs. CT alone were 82.1/74.7/76.7% vs. 75.0/66.7/68.9%, and specificity and accuracy were significantly higher for combined CT/MRI than that for CT alone (p = 0.03 and 0.008, respectively). Similarly, sensitivity/specificity/accuracy values were significantly higher for combined CT/MRI among urologists (78.6/73.3/74.8% vs. 64.3/64.0/64.1%, p = 0.04/0.04/0.008). However, sensitivity/specificity/accuracy did not significantly differ between the two among radiologists (89.3/74.7/78.6% vs. 85.7/73.3/76.7%, p = 0.32/0.56/0.32). CONCLUSIONS: Combined CT/MRI is useful for diagnosing malignancy in patients with CRMs using BC2019, especially for non-expert readers.

A role of the cspA gene encoding a mycolyltransferase in the growth under alkaline conditions of Corynebacterium glutamicum.

Corynebacterium glutamicum is widely used in the industrial production of amino acids. Producer strains are generated by classical random mutagenesis, and therefore have detrimental characteristics caused by unnecessary mutations. Increased alkali sensitivity is one of those undesired characteristics. We found that one of the laboratory strains, AJ12036DeltacspADeltacspB, showed decreased growth under alkaline conditions. To clarify which mutation is responsible for alkali sensitivity, we constructed mutant strains carrying the DeltacspA and/or DeltacspB mutations from wild-type strain ATCC13869. We found that disruption of cspA encoding a mycolyltransferase alone caused increased alkali sensitivity. The DeltacspA mutant also showed increased susceptibility to ethambutol, penicillin, and rifampicin. Disruption of cspB had no effect on alkali sensitivity or drug sensitivity. These results indicate that the mycolate layer is important for alkali sensitivity as well as drug susceptibility in this bacterium.

Outcome of patients with lung cancer and severe psychiatric disorder admitted to a medical psychiatric unit.

The purpose of the present study was to evaluate the clinical profiles and treatment outcomes of patients with lung cancer admitted to the Medical Psychiatric Unit (MPU), which is built for patients with physical and severe psychiatric disorders. All medical records of patients with lung cancer admitted to the MPU of Tachikawa hospital were reviewed. The clinical outcomes of these patients were retrospectively evaluated between January 2010 and December 2016. A total of 24 patients in the MPU were histologically or cytologically diagnosed with primary lung cancer. Of these, 20 patients had schizophrenia, and 4 patients had a mood disorder. There were 15 patients who were diagnosed using bronchoscopy. The histology indicated adenocarcinoma, squamous cell carcinoma and non-small-cell lung cancer-not otherwise specified were in 11, 8, and 1 patient, respectively, while small-cell lung cancer was indicated in 4 patients. Surgery, chemoradiotherapy, radiotherapy, chemotherapy was performed in 13, 4, 2, 1 and 4 patients, respectively. The median survival time was 76.7 months for patients who underwent surgery, while it was 14.4 months for those who underwent chemoradiotherapy. In the MPU, patients with lung cancer and severe psychiatric disorders could be safely diagnosed, and patients with early-stage lung cancer exhibited long-term survival.

Persistence of positive severe acute respiratory syndrome coronavirus-2 reverse transcription-polymerase chain reaction test result for 24 days in a hospitalized asymptomatic carrier.

BACKGROUND: Several countries have imposed a mandatory 14-day period of quarantine on individuals arriving from countries considered high-risk for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. However, it is not clear how long asymptomatic patients infected with SARS-CoV-2 can be an asymptomatic carrier. CASE PRESENTATION: We experienced a case of an asymptomatic female patient infected with SARS-CoV-2 with abnormal chest computed tomography findings. She did not develop a fever during hospitalized isolation. She remained reverse transcription-polymerase chain reaction-positive for 24 days. CONCLUSION: An asymptomatic patient diagnosed with SARS-CoV-2 infection remained reverse transcription-polymerase chain reaction-positive for 24 days, although she was quarantined in an isolation hospital. This finding suggests that an asymptomatic patient diagnosed with SARS-CoV-2 infection with abnormal chest computed tomography findings can be an asymptomatic carrier for more than 3 weeks.

Crystallization of CprB, an autoregulator-receptor protein from Streptomyces coelicolor A3(2).

CprB, an autoregulator-receptor protein from Streptomyces coelicolor A3(2), was crystallized by the hanging-drop vapour-diffusion method using polyethylene glycol 6000 as a precipitant. Three crystal forms (I, II and III) were obtained; crystal forms I and II were useful for structure determination. Form I crystals belong to an orthorhombic system with space group P2(1)2(1)2(1) and diffracted to better than 2.4 A. Form II crystals belong to a tetragonal system with space group P4(1(3))2(1)2.