Prophylactic intestinal decontamination with oral ciprofloxacin is effective in the prevention of bacterial infections in patients with cirrhosis who were suffering from acute upper gastrointestinal hemorrhage.

Gall-bladder wall thickening is commonly seen in patients with cirrhosis, but its exact causes have not been well established. We evaluated clinical, biochemical and haemodynamic data of patients with cirrhosis with respect to the presence of thickening of the gall-bladder wall. After excluding patients who presented with gallstones, acute or chronic cholecystitis, heart failure, a serum creatinine level greater than 2 mg/dL and/or a serum alanine aminotransferase level greater than 400 U/L, 77 patients with cirrhosis (75 male, two female; mean age 58 +/- 8 years) were enrolled in the study. Clinical, biochemical, ultrasound and haemodynamic data were obtained in every patient. Forty-one (53%) of 77 patients with cirrhosis had gall-bladder wall thickening (> 4 mm). Compared with patients with a normal gall-bladder wall, patients with gall-bladder wall thickening had significantly lower serum albumin levels (3.6 +/- 0.6 vs 2.9 +/- 0.7 gm/dL, respectively; P < 0.05), a longer prothrombin time (13 +/- 6 vs 16 +/- 6 s, respectively; P < 0.05), more patients with Child-Pugh class C (6 vs 37%, respectively; P < 0.05) and more patients with ascites (8 vs 50%, respectively; P < 0.05). In addition, compared with patients with a normal gall-bladder wall, those patients with gall-bladder wall thickening had a higher hepatic venous pressure gradient (13.9 +/- 4.5 vs 17.1 +/- 4.1 mmHg, respectively; P < 0.01) and a lower systemic vascular resistance (SVR; 1144 +/- 332 vs 1010 +/- 318 dyn.s/cm5, respectively; P < 0.05). Using a multivariate analysis, the presence of ascites and SVR lower than 900 dyn.s/cm5 were independently correlated with the presence of gall-bladder wall thickening, while a hepatic vein pressure gradient greater than 10 mmHg had only a marginally significant association. The presence of ascites, decreased SVR and portal hypertension are related to the occurrence of gall-bladder wall thickening in patients with cirrhosis, indicating that the development of gall-bladder wall thickening may be multifactorial.

Obstructive sleep apnea (OSA) has been a common sleep disorder for years, and polysomnography (PSG) remains the gold standard for diagnosing OSA. Nevertheless, PSG is a time and money consuming test, and patients have to wait long for arranging a PSG test in a hospital. In light of this, portable and wearable tools for OSA classification have been developed recently as a low‐cost and easy‐to‐use screening method before undergoing PSG. Using unsegmented electrocardiogram (ECG) signals, a deep neural network (DNN)‐based model is developed here to categorize OSA severity with the following features. First, the model takes unsegmented ECG signals recorded overnight as input, and then generates a four‐level scale as output. Since all the input ECG signals are unsegmented, the tremendous amount of effort spent on signal annotation can be fully saved. Second, the largest amount of data is used to test the model and consequently provide a high generalization ability, as compared with others in the literature. The overall outperformance of this work is highlighted at the end of this article, and this work is validated as an easy‐to‐use and effective screening tool for OSA accordingly.

From August 1988 to April 1989, we observed 52 patients who developed so-called 'needle fainting' (or what the Chinese call 'Yun-Cheng' phenomenon) 55 times among a total sample of 28,285 procedures of acupuncture therapy at the Center for Traditional Medicine of Veterans General Hospital in Taipei. Of these syncopal patients, 35 were male and 17 were female. Their mean age was 45 years (with a range of 11 to 72 years). All patients were in an upright position when needle fainting occurred. Their usual manifestations were pallor, cold sweating, nausea, and bradycardia. They all recovered soon after lying down; no one developed a complete loss of consciousness. No mortality was noted. When comparing the patients who experienced syncope during their first visit to our Clinic (Group I, n = 27) with the patients who experienced syncope in a follow-up treatment (Group II, n = 25; 3 patients had 2 episodes in sequential treatments), we found a significantly higher incidence of needle fainting (p less than 0.0001) in Group I patients (27 out of 2,855 or 0.94%) than in Group II patients (28 out of 25,430 or 0.11%). The mean age of Group I patients (39 +/- 15.4 years) was significantly less than that of Group II patients (51.6 +/- 18.0 years) (p less than 0.001). The coexistence of other medical problems was significantly higher in Group II patients (72%) than in Group I patients (18.5%) (p less than 0.0001).

Plasma levels of substance P are elevated in patients with nonalcoholic cirrhosis and may play an important role in the pathogenesis of spider angiomas.

No abstract

Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women.

A new RNN-based prosodic information synthesizer for Mandarin Chinese text-to-speech (TTS) is proposed in this paper. Its four-layer recurrent neural network (RNN) generates prosodic information such as syllable pitch contours, syllable energy levels, syllable initial and final durations, as well as intersyllable pause durations. The input layer and first hidden layer operate with a word-synchronized clock to represent currentword phonologic states within the prosodic structure of text to be synthesized. The second hidden layer and output layer operate on a syllable-synchronized clock and use outputs from the preceding layers, along with additional syllable-level inputs fed directly to the second hidden layer, to generate desired prosodic parameters. The RNN was trained on a large set of actual utterances accompanied by associated texts, and can automatically learn many human-prosody phonologic rules, including the wellknown Sandhi Tone 3 F0-change rule. Experimental results show that all synthesized prosodic parameter sequences matched quite well with their original counterparts, and a pitch-synchronousoverlap-add-based (PSOLA-based) Mandarin TTS system was also used for testing of our approach. While subjective tests are difficult to perform and remain to be done in the future, we have carried out informal listening tests by a significant number of native Chinese speakers and the results confirmed that all synthesized speech sounded quite natural.