Whole genomic analysis reveals atypical non-homologous off-target large structural variants induced by CRISPR-Cas9-mediated genome editing.

CRISPR-Cas9 genome editing has promising therapeutic potential for genetic diseases and cancers, but safety could be a concern. Here we use whole genomic analysis by 10x linked-read sequencing and optical genome mapping to interrogate the genome integrity after editing and in comparison to four parental cell lines. In addition to the previously reported large structural variants at on-target sites, we identify heretofore unexpected large chromosomal deletions (91.2 and 136 Kb) at atypical non-homologous off-target sites without sequence similarity to the sgRNA in two edited lines. The observed large structural variants induced by CRISPR-Cas9 editing in dividing cells may result in pathogenic consequences and thus limit the usefulness of the CRISPR-Cas9 editing system for disease modeling and gene therapy. In this work, our whole genomic analysis may provide a valuable strategy to ensure genome integrity after genomic editing to minimize the risk of unintended effects in research and clinical applications.

Cardiothoracic ratio values and trajectories are associated with risk of requiring dialysis and mortality in chronic kidney disease.

BACKGROUND: The prognostic role of the cardiothoracic ratio (CTR) in chronic kidney disease (CKD) remains undetermined. METHODS: We conducted a retrospective cohort study of 3117 patients with CKD aged 18-89 years who participated in an Advanced CKD Care Program in Taiwan between 2003 and 2017 with a median follow up of 1.3(0.7-2.5) and 3.3(1.8-5.3) (IQR) years for outcome of end-stage renal disease (ESRD) and overall death, respectively. We developed a machine learning (ML)-based algorithm to calculate the baseline and serial CTRs, which were then used to classify patients into trajectory groups based on latent class mixed modelling. Association and discrimination were evaluated using multivariable Cox proportional hazards regression analyses and C-statistics, respectively. RESULTS: The median (interquartile range) age of 3117 patients is 69.5 (59.2-77.4) years. We create 3 CTR trajectory groups (low [30.1%], medium [48.1%], and high [21.8%]) for the 2474 patients with at least 2 CTR measurements. The adjusted hazard ratios for ESRD, cardiovascular mortality, and all-cause mortality in patients with baseline CTRs ≥0.57 (vs CTRs <0.47) are 1.35 (95% confidence interval, 1.06-1.72), 2.89 (1.78-4.71), and 1.50 (1.22-1.83), respectively. Similarly, greater effect sizes, particularly for cardiovascular mortality, are observed for high (vs low) CTR trajectories. Compared with a reference model, one with CTR as a continuous variable yields significantly higher C-statistics of 0.719 (vs 0.698, P = 0.04) for cardiovascular mortality and 0.697 (vs 0.693, P < 0.001) for all-cause mortality. CONCLUSIONS: Our findings support the real-world prognostic value of the CTR, as calculated by a ML annotation tool, in CKD. Our research presents a methodological foundation for using machine learning to improve cardioprotection among patients with CKD.

An enlarged heart occurs during various medical conditions and can result in early death. However, it is unclear whether this is also the case in patients with chronic kidney disease (CKD). Although the size of the heart can be measured on chest X-rays, this process is time consuming. We used artificial intelligence to quantify the heart size of 3117 CKD patients based on their chest X-rays within hours. We found that CKD patients with an enlarged heart were more likely to develop end-stage kidney disease or die. This could improve monitoring of CKD patients with an enlarged heart and improve their care.

Application of machine learning methods for the prediction of true fasting status in patients performing blood tests.

The fasting blood glucose (FBG) values extracted from electronic medical records (EMR) are assumed valid in existing research, which may cause diagnostic bias due to misclassification of fasting status. We proposed a machine learning (ML) algorithm to predict the fasting status of blood samples. This cross-sectional study was conducted using the EMR of a medical center from 2003 to 2018 and a total of 2,196,833 ontological FBGs from the outpatient service were enrolled. The theoretical true fasting status are identified by comparing the values of ontological FBG with average glucose levels derived from concomitant tested HbA1c based on multi-criteria. In addition to multiple logistic regression, we extracted 67 features to predict the fasting status by eXtreme Gradient Boosting (XGBoost). The discrimination and calibration of the prediction models were also assessed. Real-world performance was gauged by the prevalence of ineffective glucose measurement (IGM). Of the 784,340 ontologically labeled fasting samples, 77.1% were considered theoretical FBGs. The median (IQR) glucose and HbA1c level of ontological and theoretical fasting samples in patients without diabetes mellitus (DM) were 94.0 (87.0, 102.0) mg/dL and 5.6 (5.4, 5.9)%, and 92.0 (86.0, 99.0) mg/dL and 5.6 (5.4, 5.9)%, respectively. The XGBoost showed comparable calibration and AUROC of 0.887 than that of 0.868 in multiple logistic regression in the parsimonious approach and identified important predictors of glucose level, home-to-hospital distance, age, and concomitantly serum creatinine and lipid testing. The prevalence of IGM dropped from 27.8% based on ontological FBGs to 0.48% by using algorithm-verified FBGs. The proposed ML algorithm or multiple logistic regression model aids in verification of the fasting status.

Artificial intelligence reveals environmental constraints on colour diversity in insects.

Explaining colour variation among animals at broad geographic scales remains challenging. Here we demonstrate how deep learning-a form of artificial intelligence-can reveal subtle but robust patterns of colour feature variation along an ecological gradient, as well as help identify the underlying mechanisms generating this biogeographic pattern. Using over 20,000 images with precise GPS locality information belonging to nearly 2,000 moth species from Taiwan, our deep learning model generates a 2048-dimension feature vector that accurately predicts each species' mean elevation based on colour and shape features. Using this multidimensional feature vector, we find that within-assemblage image feature variation is smaller in high elevation assemblages. Structural equation modeling suggests that this reduced image feature diversity is likely the result of colder environments selecting for darker colouration, which limits the colour diversity of assemblages at high elevations. Ultimately, with the help of deep learning, we will be able to explore the endless forms of natural morphological variation at unpreceded depths.

A machine learning approach for predicting urine output after fluid administration.

BACKGROUND AND OBJECTIVE: To develop a machine learning model to predict urine output (UO) in sepsis patients after fluid resuscitation. METHODS: We identified sepsis patients in the Multiparameter Intelligent Monitoring in Intensive Care-III v1.4 database according to the Sepsis-3 criteria. We focused on two outcomes: whether the UO decreased after fluid administration and whether oliguria (defined as UO less than the threshold of 0.5 mL/kg/h) developed. A gradient tree-based machine learning model implemented with an eXtreme Gradient Boosting algorithm was used to integrate relevant physiological parameters for predicting the aforementioned outcomes. A confusion matrix was computed. RESULTS: A total of 232,929 events in 19,275 patients were included. Using decreased UO as the outcome measure, the optimal model achieved an area under the curve (AUC) of 0.86; for predicting oliguria, most models achieved an AUC greater than 0.86, and the highest sensitivity was 92.2% when the model was applied to patients with baseline oliguria. CONCLUSIONS: Machine learning could help clinicians evaluate fluid status in sepsis patients after fluid administration, thus preventing fluid overload-related complications.

Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning.

Prediction of kidney function and chronic kidney disease (CKD) through kidney ultrasound imaging has long been considered desirable in clinical practice because of its safety, convenience, and affordability. However, this highly desirable approach is beyond the capability of human vision. We developed a deep learning approach for automatically determining the estimated glomerular filtration rate (eGFR) and CKD status. We exploited the transfer learning technique, integrating the powerful ResNet model pretrained on an ImageNet dataset in our neural network architecture, to predict kidney function based on 4,505 kidney ultrasound images labeled using eGFRs derived from serum creatinine concentrations. To further extract the information from ultrasound images, we leveraged kidney length annotations to remove the peripheral region of the kidneys and applied various data augmentation schemes to produce additional data with variations. Bootstrap aggregation was also applied to avoid overfitting and improve the model's generalization. Moreover, the kidney function features obtained by our deep neural network were used to identify the CKD status defined by an eGFR of <60 ml/min/1.73 m2. A Pearson correlation coefficient of 0.741 indicated the strong relationship between artificial intelligence (AI)- and creatinine-based GFR estimations. Overall CKD status classification accuracy of our model was 85.6% -higher than that of experienced nephrologists (60.3%-80.1%). Our model is the first fundamental step toward realizing the potential of transforming kidney ultrasound imaging into an effective, real-time, distant screening tool. AI-GFR estimation offers the possibility of noninvasive assessment of kidney function, a key goal of AI-powered functional automation in clinical practice.

Improving Automatic Tremor and Movement Motor Disorder Severity Assessment for Parkinson's Disease with Deep Joint Training.

Parkinson's disease (PD) is one of the most severe and common disease globally. PD induces motor system impairment causing symptoms such as shaking, rigidity, slowness of movement, body tremor and difficulty with walking. Clinically, accurately and objectively assessing the severity of PD symptoms is critical in controlling appropriate dosage of Levodopa to prevent unwanted side effect of switching between Dyskinesia and PD. The unified Parkinson's disease rating scale published by the Movement Disorder Society (MDS-UPDRS) is an validated instrument regularly administrated by trained physician to assess the severity of a PD patient's motor disorder. In this work, we aim at advancing vision-based automatic motor disorder assessment, specifically hand tremor and movement, for PD patients during UPDRS. Our proposed method leverages information across the two behavior tasks simultaneously via deep joint training to improve each single task's, i.e., tremor and movement, severity classification rate. We evaluate our framework on a large cohort of 106 PD patients, and with our proposed deep joint training framework, we achieve accuracy of 78.01% and 80.60% in right and left hand movement binary classification; in terms of tremor severity classification, our approach obtains an enhanced recognition rates of 72.20% and 71.10% for right and left hand respectively.

Advanced Photoemission Spectroscopy Investigations Correlated with DFT Calculations on the Self-Assembly of 2D Metal Organic Frameworks Nano Thin Films.

Metal-organic frameworks (MOFs) deposited from solution have the potential to form 2-dimensional supramolecular thin films suitable for molecular electronic applications. However, the main challenges lie in achieving selective attachment to the substrate surface, and the integration of organic conductive ligands into the MOF structure to achieve conductivity. The presented results demonstrate that photoemission spectroscopy combined with preparation in a system-attached glovebox can be used to characterize the electronic structure of such systems. The presented results demonstrate that porphyrin-based 2D MOF structures can be produced and that they exhibit similar electronic structure to that of corresponding conventional porphyrin thin films. Porphyrin MOF multilayer thin films were grown on Au substrates prefunctionalized with 4-mercaptopyridine (MP) via incubation in a glovebox, which was connected to an ultrahigh vacuum system outfitted with photoelectron spectroscopy. The thin film growth process was carried out in several sequential steps. In between individual steps the surface was characterized by photoemission spectroscopy to determine the valence bands and evaluate the growth mode of the film. A comprehensive evaluation of X-ray photoemission spectroscopy (XPS), ultraviolet photoelectron spectroscopy (UPS), and inverse photoemission spectroscopy (IPES) data was performed and correlated with density functional theory (DFT) calculations of the density of states (DOS) of the films involved to yield the molecular-level insights into the growth and the electronic properties of MOF-based 2D thin films.

Acute appendicitis in organ transplantation patients: a report of two cases and a literature review.

BACKGROUND: Abdominal surgery on patients with previous organ transplantation, especially in the early postoperative period, is a challenging problem. Due to high risk of complications in transplant patients, we usually tend to treat such patients more conservatively rather compared to the more aggressive attitude in diagnosis and surgery of non-transplant patients. Delayed diagnosis, delayed surgery, and high morbidity and mortality are more common in transplant patients with GI disease. While appendicitis is one of the most common surgical diseases, with an estimated lifetime risk of 8.6% for males and 6.7% for females, there are relatively few reports of appendicitis in solid organ transplant recipients, and the condition has rarely been reported after liver transplantation. CASE REPORT: We have performed surgery on 2 cases of presumed acute appendicitis among 75 cases of kidney and liver transplantation in our series in the last 10 years. Laparoscopic technique was used for exploration of presumed acute appendicitis with atypical clinical and image presentation in a deceased donor liver transplantation (DDLT) and a deceased donor kidney transplantation (DDKT). CONCLUSIONS: Acute appendicitis in both patients was highly suspected preoperatively in computed tomography, and early exploration with laparoscopic technique prompted early diagnosis and treatment, with excellent surgical outcomes.

Donor-derived Cryptococcus infection in liver transplant: case report and literature review.

Cryptococcosis occurring within 30 days after transplant is unusual. We present a case of cryptococcosis diagnosed within 2 weeks of liver transplant and cryptococcal infection transmitted by liver transplant is considered as the cause. A 63-year-old woman with hepatitis C virus-related cirrhosis and hepatocellular carcinoma had an orthotopic liver transplant from a 45-year-old donor. The immediate postoperative course was smooth, although she was confused with a fever, tachycardia, respiratory failure of 1 week's duration after the orthotopic liver transplant. A liver biopsy was performed for hyperbilirubinemia 2 weeks after the orthotopic liver transplant that showed a Cryptococcus-like yeast. Her blood culture was reexamined, and it was confirmed as Cryptococcus neoformans that had been misinterpreted as candida initially. At the time of the re-examination, her sputum was clear. We checked her preoperative blood sample, retrospectively, for serum cryptococcal antigen with negative result. She was on liposomal amphotericin treatment for 1 month when her blood culture became negative. She was discharged home, with good liver function and a low antigen titer for cryptococcal infection. Cryptococcal disease usually develops at a mean of 5.6 months after transplant. However an early occurrence is rare. Apart from that, its variable clinical presentations make early detection difficult. It might be an early reactivation or a donor-derived infection. The latter usually occurs in unusual sites (eg, the transplanted organ as the sole site of involvement). Our case presented as cryptococcoma and liver involvement was diagnosed by an unintentional liver biopsy.

Ewald mesh method for quantum mechanical calculations.

The Fourier transform Coulomb (FTC) method has been shown to be effective for the fast and accurate calculation of long-range Coulomb interactions between diffuse (low-energy cutoff) densities in quantum mechanical (QM) systems. In this work, we split the potential of a compact (high-energy cutoff) density into short-range and long-range components, similarly to how point charges are handled in the Ewald mesh methods in molecular mechanics simulations. With this linear scaling QM Ewald mesh method, the long-range potential of compact densities can be represented on the same grid as the diffuse densities that are treated by the FTC method. The new method is accurate and significantly reduces the amount of computational time on short-range interactions, especially when it is compared to the continuous fast multipole method.

Folate deprivation promotes mitochondrial oxidative decay: DNA large deletions, cytochrome c oxidase dysfunction, membrane depolarization and superoxide overproduction in rat liver.

Little is known about the biological effect of folate in the protection against mitochondrial (mt) oxidative decay. The objective of the present study was to examine the consequence of folate deprivation on mt oxidative degeneration, and the mechanistic link underlying the relationship. Male Wistar rats were fed with an amino acid-defined diet containing either 8 (control) or 0 (folate-deficient, FD) mg folic acid/kg diet. After a 4-week FD feeding period, significant elevation in oxidative stress was observed inside the liver mitochondria with a 77% decrease in mt folate level (P<0.001), a 28 % reduction in glutathione peroxidase activity (P= 0.0333), a 1.2-fold increase of mt protein carbonyls (P=0.0278) and an accumulated 4834 bp large-scale deletion in mtDNA. The elicited oxidative injuries in FD liver mitochondria were associated with 30 % reduction of cytochrome c oxidase (CcOX) activity (P=0.0264). The defective CcOX activity in FD hepatocytes coincided with mt membrane potential dissipation and intracellular superoxide elevation. Exposure of FD hepatocytes to pro-oxidant challenge (32 microM-copper sulphate for 48 h) led to a further loss in CcOX activity and mt membrane potential with a simultaneous increase in superoxide production. Preincubation of pro-oxidant-treated FD hepatocytes with supplemental folic acid (10-1000 microM) reversed the mt oxidative defects described earlier and diminished superoxide overproduction. Increased supplemented levels of folic acid strongly correlated with decreased lipid peroxidation (gamma - 0.824, P=0.0001) and protein oxidative injuries (gamma -0.865, P=0.0001) in pro-oxidant-challenged FD liver mitochondria. Taken together, the results demonstrated that folate deprivation induces oxidative stress in liver mitochondria, which is associated with CcOX dysfunction, membrane depolarization and superoxide overproduction. The antioxidant activity of supplemental folic acid may partially, if not fully, contribute to the amelioration of pro-oxidant-elicited mt oxidative decay.