X-linked ichthyosis associated with psychosis and behavioral abnormalities: a case report

Abstract Background X-linked ichthyosis is a dermatological condition caused by deficiency for the enzyme steroid sulfatase. Previously, X-linked ichthyosis/steroid sulfatase deficiency has been associated with developmental and neurological phenotypes. Here, we show for the first time, that X-linked ichthyosis may be comorbid with an additional psychiatric phenotype (psychosis). Case presentation We report the case of an 11-year-old Saudi Arabian boy with X-linked ichthyosis associated with psychosis, mental retardation, autism spectrum disorder, inattentive attention deficit hyperactivity disorder, and epilepsy. Genetic analysis revealed a 1.68 Mb deletion encompassing STS in 95% of cells while biochemical analysis revealed correspondingly low steroid sulfatase activity consistent with a diagnosis of X-linked ichthyosis. The psychotic symptoms could be reasonably well controlled by administration of an atypical antipsychotic. Conclusions This report describes a case of comorbid X-linked ichthyosis and psychosis (most closely corresponding to early-onset schizophrenia) for the first time, and suggests that deficiency for steroid sulfatase and contiguous genes may increase vulnerability to psychosis as well as other psychological disorders

Pancreatic Cancer Risk Stratification based on Patient Family History

poster abstractBackground: Pancreatic cancer is the fourth leading cause of cancer-related deaths in the US with an annual death rate approximating the incidence (38,460 and 45,220 respectively according to 2013 American Cancer Society). Due to delayed diagnosis, only 8% of patients are amenable to surgical resection, resulting in a 5-year survival rate of less than 6%. Screening the general population for pancreatic cancer is not feasible because of its low incidence (12.1 per 100,000 per year) and the lack of accurate screening tools. However, patients with an inherited predisposition to pancreatic cancer would benefit from selective screening. Methods: Clinical notes of patients from Indiana University (IU) Hospitals were used in this study. A Natural Language Processing (NLP) system based on the Unstructured Information Management Architecture framework was developed to process the family history data and extract pancreatic cancer information. This was performed through a series of NLP processes including report separation, section separation, sentence detection and keyword extraction. The family members and their corresponding diseases were extracted using regular expressions. The Stanford dependency parser was used to accurately link the family member and their diseases. Negation analysis was done using the NegEx algorithm. PancPro risk-prediction software was used to assess the lifetime risk scores of pancreatic cancer for each patient according to his/her family history. A decision tree was constructed based on these scores. Results: A corpus of 2000 reports of patients at IU Hospitals from 1990 to 2012 was collected. The family history section was present in 249 of these reports containing 463 sentences. The system was able to identify 222 reports (accuracy 87.5%) and 458 sentences (accuracy 91.36%). Conclusion: The family history risk score will be used for patients’ pancreatic cancer risk stratification, thus contributing to selective screening

Pilot based channel estimation improvement in orthogonal frequency-division multiplexing systems using linear predictive coding

Pilot based least square (LS) channel estimation is a commonly used channel estimation technique in orthogonal frequency-division multiplexing based systems due to its simplicity. However, LS estimation does not handle the noise effect and hence suffers from performance degradation. Since the channel coefficients are correlated in time and hence show a slower variation than the noise, it is possible to encode the channel using linear predictive coding (LPC) without the noise. In this work, the channel is estimated from the pilots using LS estimation and in a second step the channel’s LS estimated is encoded as LPC coefficients to produce an improved channel estimation. The estimation technique is simulated for space-time block coding (STBC) based orthogonal frequency-division multiplexing (OFDM) system and the bit error rate (BER) curves show improvement of the LPC estimation over the LS estimation of the channel

Pancreatic Cysts Identification Using Unstructured Information Management Architecture

poster abstractPancreatic cancer is one of the deadliest cancers, mostly diagnosed at late stages. Patients with pancreatic cysts are at higher risk of developing cancer and surveillance of these patients can help with early diagnosis. Much information about pancreatic cysts can be found in free text format in various medical narratives. In this retrospective study, a corpus of 1064 records from 44 patients at Indiana University Hospital from 1990 to 2012 was collected. A natural language processing system was developed and used to identify patients with pancreatic cysts. The input goes through series of tasks within the Unstructured Information Management Architecture (UIMA) framework consisting of report separation, metadata detection, sentence detection, concept annotation and writing into the database. Metadata such as medical record number (MRN), report id, report name, report date, report body were extracted from each report. Sentences were detected and concepts within each sentence were extracted using regular expression. Regular expression is a pattern of characters matching specific string of text. Our medical team assembled concepts that are used to identify pancreatic cysts in medical reports and additional keywords were added by searching through literature and Unified Medical Language System (UMLS) knowledge base. The Negex Algorithm was used to find out negation status of concepts. The 1064 reports were divided into sets of train and test sets. Two pancreatic-cyst surgeons created the gold standard data (Inter annotator agreement K=88%). The training set was analyzed to modify the regular expression. The concept identification using the NegEx algorithm resulted in precision and recall of 98.9% and 89% respectively. In order to improve the performance of negation detection, Stanford Dependency parser (SDP) was used. SDP finds out how words are related to each other in a sentence. SDP based negation algorithm improved the recall to 95.7%

PAPR Reduction of OFDM Signals Using Clipping and Coding

The problem of the high peak to average ratio (PAPR) in OFDM signals is investigated with a brief presentation of the various methods used to reduce the PAPR with special attention to the clipping method. An alternative approach of clipping is presented, where the clipping is performed right after the IFFT stage unlike the conventional clipping that is performed in the power amplifier stage, which causes undesirable out of signal band spectral growth. In the proposed method, there is clipping of samples not clipping of wave, therefore, the spectral distortion is avoided. Coding is required to correct the errors introduced by the clipping and the overall system is tested for two types of modulations, the QPSK as a constant amplitude modulation and 16QAM as a varying amplitude modulation

ANTIBIOTIC SENSITIVITY OF BACTERIAL BLOODSTREAM INFECTIONS IN THE INTENSIVE CARE UNIT PATIENTS OF UNIVERSITY HOSPITALS IN SANA'A CITY, YEMEN

Aim: High rates of morbidity and mortality are associated to bacterial bloodstream infections (B-BSI) in many hospitals, especially in the intensive care unit. This study investigated the prevalence of antibiotic- and multidrug-resistant bacteria isolated from blood samples of patients in intensive care units of university hospitals in the city of Sana'a, Yemen. Subjects and methods: A cross-sectional study was conducted on sepsis patients admitted to intensive care units in four hospitals in Sana'a, Yemen, between January 1 and April 30, 2022. The blood cultures of patients suspected of suffering from sepsis were performed. The potential bacterial pathogens were isolated and identified using standard laboratory methods, and microbial susceptibility testing was performed using the disk diffusion technique. Results: For all identified bacteria, the average resistance rate to a broad spectrum of antibiotics tested ranged from 22.5% to 98.1%, with cefazoline (98.1%) having the greatest resistance rates, followed by amoxicillin (87.2%) and cefixime (83%). Vancomycin had a resistance rate of 4.8% whereas erythromycin had a resistance rate of 75% for Gram-positive bacteria. For Gram-negative bacteria, the resistance rates to narrow spectrum antibiotics ranged from 2.3% for colistin sulphate to 84.8% for aztreonam. Our isolates' MDR rates for resistance to at least three classes of antibiotics were 52.2% and 8.7%, respectively, for resistance to 10 different classes of broad-spectrum antibiotics and their subclasses. Conclusion: Gram positive bacteria are highly resistant to erythromycin and penicillin, while gram negative organisms are highly resistant to amoxcillin+clavulanic acid, ciprofloxacin, and all generations of cephalosporins. This study highlights the significance of prompt clinical and bacteriological monitoring among patients in critical care conditions, such as ICU patients, and also illustrates the establishment and rates of Multi Drug Resistance (MDR) pathogens.                         Peer Review History: Received: 4 August 2023; Revised: 8 September; Accepted: 27 October; Available online: 15 November 2023 Academic Editor: Dr. Asia Selman Abdullah, Pharmacy institute, University of Basrah, Iraq, [email protected] Received file:                             Reviewer's Comments: Average Peer review marks at initial stage: 6.0/10 Average Peer review marks at publication stage: 7.5/10 Reviewers: Dr. Tamer Elhabibi, Suez Canal University, Egypt, [email protected] Dr. Wadhah Hassan Ali Edrees, Hajja University, Yemen, [email protected]