Characterization of three different benzimidazolium ligands and their organo-selenium complexes by using density functional theory and Raman spectroscopy

In the present study, Raman spectroscopy (RS) along with density functional theory (DFT) calculations have been performed for the successful characterization and confirmation of the formation of three different selenium-based N-heterocyclic carbene (NHC) complexes from their respective salts. For this purpose, mean RS features and DFT calculations of different ligands and their respective selenium NHC complexes are compared. The identified characteristic RS and DFT features, of each of these ligands and their selenium complexes, show that the polarizability of benzimidazolium rings increases after complex formation with selenium. This has been shown by the enhanced intensity of the associated Raman peaks, therefore, confirming the formation of newly formed bonds. The complex formation is also confirmed by the identification of several new peaks in the spectra of complexes and these Raman bands were absent in the spectra of the ligands. Moreover, Raman spectral data sets are analyzed using a multivariate data analysis technique of Principal Component Analysis (PCA) to observe the efficiency of the RS analysis. The results presented in this study have proved the RS technique, along with DFT, an undoubtedly fast approach for the confirmation of synthesis of selenium based NHC-complexes.

Synthesis, Characterization, and Evaluation of Antifungal Activity of 1-Butyl-3-hexyl-1H-imidazol-2(3H)-selenone by Surface-Enhanced Raman Spectroscopy

In the present research work, a selenium N-heterocyclic carbene (Se-NHC) complex/adduct was synthesized and characterized by using different analytical methods including FT-IR, 1HNMR, and 13CNMR. The antifungal activity of the Se-NHC complex against Aspergillus flavus (A. flavus) fungus was investigated with disc diffusion assay. Moreover, the biochemical changes occurring in this fungus due to exposure of different concentrations of the in-house synthesized compound are characterized by surface-enhanced Raman spectroscopy (SERS) and are illustrated in the form of SERS spectral peaks. SERS analysis yields valuable information about the probable mechanisms responsible for the antifungal effects of the Se-NHC complex. As demonstrated by the SERS spectra, this Se-NHC complex caused denaturation and conformational changes in the proteins as well as decomposition of the fungal cell membrane. The SERS spectra were analyzed using two chemometric tools such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). The fungal samples' SERS spectra were differentiated using PCA, while various groups of spectra were discriminated with ultrahigh sensitivity (98%), high specificity (99.7%), accuracy (100%), and area under the receiver operating characteristic curve (87%) using PLS-DA.

Assessing Household's Municipal Waste Segregation Intentions in Metropolitan Cities of Pakistan: A Structural Equation Modeling Approach

There is a growing concern about inappropriate waste disposal and its negative impact on human health and the environment. The objective of this study is to understand household waste segregation intention considering psychological, institutional, and situational factors simultaneously. Insights into the motivations of household waste segregation drivers may assist in a better knowledge of how to pursue the most efficient and effective initiatives. For this purpose, data from a representative sample comprising 849 households is obtained from the twin cities of Islamabad and Rawalpindi (Pakistan). The empirical analysis employs a Structural Equation Modeling (SEM) approach, showing that policy instruments have significant direct and indirect impacts on households' segregation intentions. The results highlight that government policy instruments strengthen personal and perceived norms for waste segregation intentions, resulting in an external intervention that would encourage intrinsic motivation. Therefore, policy actions become the main entry point for initiating waste segregation behavior. Public policy must continue to emphasize waste segregation since it may help resource recovery. This is imperative because the environment is a shared resource, and its conservation increases social welfare.

Automated Uterine Fibroids Detection in Ultrasound Images Using Deep Convolutional Neural Networks

Fibroids of the uterus are a common benign tumor affecting women of childbearing age. Uterine fibroids (UF) can be effectively treated with earlier identification and diagnosis. Its automated diagnosis from medical images is an area where deep learning (DL)-based algorithms have demonstrated promising results. In this research, we evaluated state-of-the-art DL architectures VGG16, ResNet50, InceptionV3, and our proposed innovative dual-path deep convolutional neural network (DPCNN) architecture for UF detection tasks. Using preprocessing methods including scaling, normalization, and data augmentation, an ultrasound image dataset from Kaggle is prepared for use. After the images are used to train and validate the DL models, the model performance is evaluated using different measures. When compared to existing DL models, our suggested DPCNN architecture achieved the highest accuracy of 99.8 percent. Findings show that pre-trained deep-learning model performance for UF diagnosis from medical images may significantly improve with the application of fine-tuning strategies. In particular, the InceptionV3 model achieved 90% accuracy, with the ResNet50 model achieving 89% accuracy. It should be noted that the VGG16 model was found to have a lower accuracy level of 85%. Our findings show that DL-based methods can be effectively utilized to facilitate automated UF detection from medical images. Further research in this area holds great potential and could lead to the creation of cutting-edge computer-aided diagnosis systems. To further advance the state-of-the-art in medical imaging analysis, the DL community is invited to investigate these lines of research. Although our proposed innovative DPCNN architecture performed best, fine-tuned versions of pre-trained models like InceptionV3 and ResNet50 also delivered strong results. This work lays the foundation for future studies and has the potential to enhance the precision and suitability with which UF is detected.

An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs)

Modern vehicle communication development is a continuous process in which cutting-edge security systems are required. Security is a main problem in the Vehicular Ad Hoc Network (VANET). Malicious node detection is one of the critical issues found in the VANET environment, with the ability to communicate and enhance the mechanism to enlarge the field. The vehicles are attacked by malicious nodes, especially DDoS attack detection. Several solutions are presented to overcome the issue, but none are solved in a real-time scenario using machine learning. During DDoS attacks, multiple vehicles are used in the attack as a flood on the targeted vehicle, so communication packets are not received, and replies to requests do not correspond in this regard. In this research, we selected the problem of malicious node detection and proposed a real-time malicious node detection system using machine learning. We proposed a distributed multi-layer classifier and evaluated the results using OMNET++ and SUMO with machine learning classification using GBT, LR, MLPC, RF, and SVM models. The group of normal vehicles and attacking vehicles dataset is considered to apply the proposed model. The simulation results effectively enhance the attack classification with an accuracy of 99%. Under LR and SVM, the system achieved 94 and 97%, respectively. The RF and GBT achieved better performance with 98% and 97% accuracy values, respectively. Since we have adopted Amazon Web Services, the network's performance has improved because training and testing time do not increase when we include more nodes in the network.

Detection of different chemical moieties in aqueous media by luminescent Europium as sensor

Detection of different chemical moieties especially trace metals is important for humans as well as water safety. In this review, different detectors synthesized by the combination of different ligands with luminescent europium complexes were discussed for the separation of metals and chemical moieties in aqueous media. These detectors displayed high sensitivity and selectivity. The limit-of-detection values were very low indicating that these detectors are best suitable for the sensing of chemical moieties and trace metals. These detectors’ luminescent changes could be noticed with the naked eye.

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system more congested. Content pre-caching is an existing challenge in VANET; pre-caching involves the content's early delivery to the requested vehicles to avoid network delays and control network congestion. Early content prediction saves vehicles from accidents and road disasters in urban environments. Periodic data dissemination without considering the state of the road and surrounding vehicles are considered in this research. The content available at a specified time poses considerable challenges in VANET for content delivery. To address these challenges, we propose a machine learning-based, zonal/context-aware-equipped content pre-caching strategy in this research. The proposed model improves content placement and content management in the pre-caching mode for VANET. Content caching is achieved through machine learning, which significantly improves content prediction by pre-caching the content early to the desired vehicles that are part of the zone. In this paper, three algorithms are presented, the first is zone selection using the customized algorithm, the second is the content dissemination algorithm, and the third is the content pre-caching decision algorithm using supervised machine learning that improves the early content prediction accuracy by 99.6%. The cache hit ratio for the proposed technique improves by 13% from the previous techniques. The prediction accuracy of the proposed technique is compared with CCMP, MLCP, and PCZS+PCNS on the number of vehicles from 10 to 150, with an improved average of 16%. Finally, the average delay reduces over time compared with the state-of-the-art techniques of RPSS, MLCP, CCMP, and PCZS+PCNS. Finally, the average delay shows that the proposed method effectively reduces the delay when the number of nodes increases. The proposed solution improves the content delivery request while comparing it with existing techniques. The results show improved pre-caching in VANET to avoid network congestion.

Investigating correlations between illness and defensive behaviour approach: A case of twin cities of Pakistan

Municipal solid waste (MSW) management has emerged as a major problem for modern societies in recent decades. An optimal waste management system is essential to prevent the pollution burden and associated health related issues. This study carries out an empirical evaluation of the illness caused by inadequate solid waste management in the metropolitan of Rawalpindi-Islamabad. The model is based on utility-maximizing consumer behavior and predicted probability of disease in the household is estimated by employing “seemingly uncorrelated bivariate probit model”. Primary data obtained through multistage random sampling that comprises of 849 respondents. The findings show that irregular waste disposal sites in the vicinity of residences cause illness. The key findings indicate that distance from dumpsites and use of contaminated water adversely affect the health outcomes. Furthermore, the results show that respondents were unable to engage in defensive activities due to a lack of awareness. Oft-times, the waste is dumped in illegal sites that is burnt thus causing excessive air and ground water pollution. The results shed light on the respondents' understanding of the negative consequences of excessive waste disposal and study suggests measures that motivate households to engage in defensive activities through effective campaigns and capacity building programmes that ensure sustainable solid waste management.

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An overview of the household solid waste generation and management practices.

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Construct households' illness function regarding improper waste disposal practices.

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Construct households' defensive behaviour regarding illness function.

An integrated assessment model for food security under climate change for South Asia

The present study develops an integrated assessment model (IAM) for food security under climate change for South Asia. For IAM, initially, an econometric model is estimated that identifies the impact of climate change on crop yields, using the historical relationships between temperature, precipitation, and the production of cereals. Subsequently, future projections have been collected for temperature and precipitation from climate models of the Coupled Model Inter-comparison Project Phase 5 (CMIP5), and the previous econometric model is applied to obtain the implied future cereal yields changes. Then, the yield variations are fed into a multiregional Global Trade Analysis Project (GTAP) model, calibrated to the GTAP 9 database, taking the form of decreases in factor-augmenting productivity of the grains sector. Further, the present study evaluates the effects of climate change on an individual South Asian country. The results indicate that change in climate decreases food production, increases food prices, decreases food consumption, and thus affects the welfare. Trade and fiscal policy responses are investigated to combat the problem of food security. It is revealed that these two policies fail to compensate climate change damage in all the selected South Asian countries.

Enhanced Kalman filter algorithm using fuzzy inference for improving position estimation in indoor navigation

In recent few years, the widespread applications of indoor navigation have compelled the research community to propose novel solutions for detecting objects position in the Indoor environment. Various approaches have been proposed and implemented concerning the indoor positioning systems. This study propose an fuzzy inference based Kalman filter to improve the position estimation in indoor navigation. The presented system is based on FIS based Kalman filter aiming at predicting the actual sensor readings from the available noisy sensor measurements. The proposed approach has two main components, i.e., multi sensor fusion algorithm for positioning estimation and FIS based Kalman filter algorithm. The position estimation module is used to determine the object location in an indoor environment in an accurate way. Similarly, the FIS based Kalman filter is used to control and tune the Kalman filter by considering the previous output as a feedback. The Kalman filter predicts the actual sensor readings from the available noisy readings. To evaluate the proposed approach, the next-generation inertial measurement unit is used to acquire a three-axis gyroscope and accelerometer sensory data. Lastly, the proposed approach’s performance has been investigated considering the MAD, RMSE, and MSE metrics. The obtained results illustrate that the FIS based Kalman filter improve the prediction accuracy against the traditional Kalman filter approach.

Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.

Testing the Role of Waste Management and Environmental Quality on Health Indicators Using Structural Equation Modeling in Pakistan

Improper management of municipal waste has become a growing concern globally due to its impact on the environment, health, and overall living conditions of households in cities. Waste production has increased because households do not adopt waste management practices that ensure sustainability. Previous studies on household waste management often considered socio-economic aspects and overlooked the environmental and behavioral factors influencing the disposal practices and health status. This study adopted four constructs, defensive attitude, environmental knowledge, environmental quality, and waste disposal, by employing a structural equation modeling approach to explore research objectives. Data from 849 households of the Islamabad-Rawalpindi metropolitan was collected by using a multi-stage sampling technique. The structural model results showed that the two constructs, environmental knowledge and defensive behavior, positively affect household health status. The most significant health-related considerations are waste disposal and environmental quality, both of which negatively impact health status and do not support our hypothesis. The results provide valuable perspectives to enable households to engage actively in waste management activities. The findings indicate that understanding the intentions of household health status drivers can assist policymakers and agencies in promoting an efficient and successful community programmes related to sustainable solid waste management by allowing them to foster how the desired behavior can be achieved.

Towards a Remote Monitoring of Patient Vital Signs Based on IoT-Based Blockchain Integrity Management Platforms in Smart Hospitals

Over the past several years, many healthcare applications have been developed to enhancethe healthcare industry. Recent advancements in information technology and blockchain technologyhave revolutionized electronic healthcare research and industry. The innovation of miniaturizedhealthcare sensors for monitoring patient vital signs has improved and secured the human healthcaresystem. The increase in portable health devices has enhanced the quality of health-monitoringstatus both at an activity/fitness level for self-health tracking and at a medical level, providing moredata to clinicians with potential for earlier diagnosis and guidance of treatment. When sharingpersonal medical information, data security and comfort are essential requirements for interactionwith and collection of electronic medical records. However, it is hard for current systems to meetthese requirements because they have inconsistent security policies and access control structures.The new solutions should be directed towards improving data access, and should be managed bythe government in terms of privacy and security requirements to ensure the reliability of data formedical purposes. Blockchain paves the way for a revolution in the traditional pharmaceuticalindustry and benefits from unique features such as privacy and transparency of data. In this paper,we propose a novel platform for monitoring patient vital signs using smart contracts based onblockchain. The proposed system is designed and developed using hyperledger fabric, which isan enterprise-distributed ledger framework for developing blockchain-based applications. Thisapproach provides several benefits to the patients, such as an extensive, immutable history log, andglobal access to medical information from anywhere at any time. The Libelium e-Health toolkitis used to acquire physiological data. The performance of the designed and developed system isevaluated in terms of transaction per second, transaction latency, and resource utilization usinga standard benchmark tool known as Hyperledger Caliper. It is found that the proposed systemoutperforms the traditional health care system for monitoring patient data.

The effect of task symmetry on bimanual reach-to-grasp movements after cervical spinal cord injury

Injury to the cervical spinal cord results in deficits in bimanual control, reducing functional independence and quality of life. Despite this, little research has investigated the control strategies which underpin bimanual arm/hand movements following cervical spinal cord injury (cSCI). Using kinematics and surface electromyography this study explored how task symmetry affects bimanual control, in patients with an acute cSCI (< 6 m post injury), as they performed naturalistic bimanual reach-to-grasp actions (to objects at 50% and 70% of their maximal reach distance), and how this differs compared to uninjured age-matched controls. Twelve adults with a cSCI (mean age 69.25 years), with lesions at C3–C8, categorized by the American Spinal Injury Impairment Scale (AIS) at C or D and 12 uninjured age-matched controls (AMC) (mean age 69.29 years) were recruited. Participants with a cSCI produced reach-to-grasp actions which took longer, were slower, less smooth and had longer deceleration phases than AMC (p < 0.05). Participants with a cSCI were less synchronous than AMC at peak velocity and just prior to object pick up (p < 0.05), but both groups ended the movement in a synchronous fashion. Peak muscle activity occurred just prior to object pick up for both groups. While there seems to be a greater reliance on the deceleration phase of the movement, we observed minimal disruption of the more impaired limb on the less impaired limb and no additional effects of task symmetry on bimanual control. Further research is needed to determine how to take advantage of this retained bimanual control in therapy.

Crystallization and preliminary X-ray diffraction analysis of L-threonine dehydrogenase (TDH) from the hyperthermophilic archaeon Thermococcus kodakaraensis

The enzyme L-threonine dehydrogenase catalyses the NAD(+)-dependent conversion of L-threonine to 2-amino-3-ketobutyrate, which is the first reaction of a two-step biochemical pathway involved in the metabolism of threonine to glycine. Here, the crystallization and preliminary crystallographic analysis of L-threonine dehydrogenase (Tk-TDH) from the hyperthermophilic organism Thermococcus kodakaraensis KOD1 is reported. This threonine dehydrogenase consists of 350 amino acids, with a molecular weight of 38 kDa, and was prepared using an Escherichia coli expression system. The purified native protein was crystallized using the hanging-drop vapour-diffusion method and crystals grew in the tetragonal space group P4(3)2(1)2, with unit-cell parameters a = b = 124.5, c = 271.1 A. Diffraction data were collected to 2.6 A resolution and preliminary analysis indicates that there are four molecules in the asymmetric unit of the crystal.

Management of immature heterotopic ossification (HO) of the hip

A case of extensive heterotopic ossification involving the left hip in a 16 year old girl who sustained non traumatic spinal paralysis at T4 ASIA scale A. This case demonstrates the practical difficulties facing clinicians involved in the rehabilitation of this paraplegic patient who required intervention before full maturation of her left hip heterotopic ossification (HO). The patient was developing a rapidly progressive fixed scoliosis and severe difficulty in achieving a proper seating posture. In addition there was difficulty with the application of a suitable orthosis to try and limit the progression of scoliosis as a result of the HO. Discussants will comment on heterotopic ossification in general and the course of action in this particular case.

CD9 immunohistochemical staining of breast carcinoma: unlikely to provide useful prognostic information for routine use

AIMS

CD9, a cell membrane glycoprotein, is found in a variety of tumour cells and is believed to regulate cell motility and possibly cell growth. It has been reported that the absence of CD9 is associated with increased aggressiveness of breast carcinoma, but no detailed studies of the distribution of CD9 in normal and abnormal breast tissue are available. This investigation was aimed at studying the distribution of CD9 in a wide variety of breast biopsies including normal, benign, and malignant cases, and assessing its usefulness as a prognostic marker in breast cancer.

METHODS AND RESULTS

Sections of 113 breast biopsies from female and male patients including 10 normal, 23 benign, and 80 malignant cases were examined. The monoclonal antibody CD9 (Novacastra Ltd, Newcastle-upon-Tyne, UK) was used with the avidin-biotin complex immunoperoxidase technique. The results were assessed semiquantitatively using a four scale system of 3+, 2+, 1+ and negative. All normal and benign epithelial cells were strongly stained (3+). In female breast carcinomas, 40% were 3+, 49% were 2+, and 11% were 1+. Both cases of male breast carcinomas scored 3+. For female breast cancers, the results were then correlated to tumour grade, the presence or absence of lymph node metastases, and oestrogen and progesterone receptor status. No significant statistical correlation was found with any of these parameters. We then examined 11 axillary lymph nodes with metastases from some of the above cases. Three of these cases had a CD9 score of 3+, seven were 2+, and one was 1+. The metastatic tumours in all 11 cases were strongly stained (3+).

CONCLUSIONS

Immunostaining for CD9 is unlikely to provide any useful additional prognostic information for clinical purposes.

Towards a catheter free status in neurogenic bladder dysfunction: a review of bladder management options in spinal cord injury (SCI)

STUDY DESIGN

Review.

OBJECTIVES

To assess current available options for bladder management in SCI patients the post-acute phase.

METHODS

Relevant articles were extracted from medline and Cinahl between 1966--1999. In addition, references earlier than 1966 that were listed in these articles were identified and extracted.

RESULTS

Catheterisation (indwelling or self intermittent) is still carried out by the majority of SCI patients with more morbidity for indwelling catheterisation. Other methods include condom drainage, suprapubic tapping and supreapubic pressure are used and are associated with less complications. Complicated procedures like sacral anterior root stimulator and entero-cystoplasty are carried out with the onset of or impending complications.

CONCLUSION

Several methods of bladder management are available in the post-acute phase of SCI. The method used has to be based on urodynamic characteristics with the aim of producing a continent bladder with adequate low pressure storage capacity. Modern management of the bladder in SCI has successfully reduced renal related mortality in SCI from 95% in the first half of the 20th century to the present 3%.

Consistency of physicians' legal standard and personal judgments of competency in patients with Alzheimer's disease

OBJECTIVES

To investigate the consistency of physician judgments of treatment consent capacity (competency) for patients with Alzheimer's disease (AD) when specific legal standards (LS) for competency are used, and to identify the LS most clinically relevant to experienced physicians.

DESIGN

Control and AD patient participants were videotaped being administered a measure of capacity to consent to medical treatment. Study physicians viewed videotapes of these assessments individually and made competency judgments for each participant under different LS followed by their own personal judgment of competency.

SETTING

A university medical center.

PARTICIPANTS

Participants were 10 older controls and 21 patients with AD (10 with mild and 11 with moderate AD). Five physicians with experience assessing the competency of AD patients were recruited from the geriatric psychiatry, geriatric medicine, and neurology services of a university medical center.

MEASUREMENTS

The 31 participants were videotaped performing on a measure of treatment consent capacity (Capacity to Consent to Treatment Instrument) (CCTI). The CCTI consists of two clinical vignettes (A-neoplasm and B-cardiac) that test competency under five LS. Vignette A and B assessments were videotaped separately for each participant (total videotapes for sample = 62). Each study physician viewed each videotaped vignette individually, made judgments under each of the LS (competent or incompetent), and then made his/her own personal competency judgment. Physicians were blinded to participant diagnosis. Within participant group, consistency of physician judgments was evaluated across LS and personal judgments using percentage agreement and kappa. Agreement between personal and LS judgments for the AD group was evaluated for each physician using logistic regression.

RESULTS

As expected, physicians as a group generally demonstrated very high percentage agreement in their LS and personal competency judgments for the control group. For the AD group, mean percentage judgment agreement among physicians ranged from a high of 84% (LS1) (evidencing a treatment choice) to a low of 67% (LS3) (appreciating consequences of treatment choice). Mean percentage agreement for personal competency judgments was 76%. For the AD sample, kappa analyses for physicians as a group demonstrated significant agreement not attributable to chance for LS5 (understanding treatment situation/choices) (k = 0.57, P = .001), LS4 (providing rational reasons for treatment choice) (k = 0.39, P = .04), and also for personal judgments (k = 0.48, P = .009). Analysis of LS judgment agreement within physician indicated that physicians applied the LS as discrete standards. Within-physician and for the AD sample, personal competency judgments were associated significantly with judgments on LS5 (P = .001), LS4 (P = .004), and LS3 (P < .04).

CONCLUSIONS

Experienced physicians demonstrated significant agreement assessing competency in AD patients when judgments were based upon specific legal standards. Personal competency judgments of physicians showed a substantially higher level of agreement than found in a previous study, where specific LS were not used. These results suggest that consistency of physician competency judgments can be enhanced if they are guided by knowledge of specific LS. Physicians' personal competency judgments were most closely associated with comprehension and reasoning LS, the most conservative and clinically appropriate standards for deciding competency.

Subjective versus objective memory change after temporal lobe epilepsy surgery

OBJECTIVE

To examine subjective versus objective memory change after anterior temporal lobectomy (ATL).

METHODS

A prospective, controlled study. Controls included 39 unoperated patients with intractable temporal lobe epilepsy (TLE) who were administered a series of cognitive and health-related quality of life measures at baseline and at 12-month follow-up intervals. The surgery sample included 65 patients with intractable, focal TLE who had undergone either a right or left ATL. These patients were tested preoperatively and at 6-month follow-up intervals. Subjective and objective memory change was quantified using a newly developed methodology to control for practice effect and regression to the mean.

RESULTS

Measures of subjective and objective memory change were not significantly related in the surgery sample. Prevalence of significant subjective memory decline 1 year after surgery ranged from 3 to 7%, whereas prevalence of significant objective memory decline ranged from 26 to 55%. Postoperative levels of emotional distress significantly predicted self-reported memory decline 1 year after ATL. Postoperative medication side effect and seizure outcome were also related significantly to subjective memory change in patients who had undergone left ATL.

CONCLUSIONS

Subjective and objective memory change after temporal lobectomy are not related. Complaints of significant memory decline after ATL are infrequent and may serve as a marker for depression or other mood disorder rather than organically based memory decline.

Natural-fill urodynamics in chronically catheterized patients with spinal-cord injury

OBJECTIVE

To determine whether an indwelling catheter on free drainage provides a constantly low intravesical pressure in patients with a neuropathic bladder.

PATIENTS AND METHODS

Thirty patients with complete spinal-cord injury (SCI) whose bladders were managed exclusively with an indwelling catheter were assessed urodynamically using natural-fill urodynamics (ambulatory monitoring) while their catheters were left on free drainage. Their upper urinary tracts were assessed using plain X-rays and ultrasonography.

RESULTS

Detrusor contractions causing intravesical pressure rises of >40 cmH2O for up to 4.5 min were observed in 11 patients. Renal scarring was observed in nine patients; of these, six were in the group with contractions of > 40 cmH2O, whereas only five of 21 patients with normal kidneys had such pressure rises.

CONCLUSION

An indwelling catheter on free drainage is no guarantee of a constantly low intravesical pressure. This study provides evidence to suggest that there is an association between phasic bladder contractions which occur despite catheter drainage and upper urinary tract damage in permanently catheterized patients with SCI.