Hexyl aminolevulinate, 5-aminolevulinic acid nanoemulsion, and methyl aminolevulinate in photodynamic therapy of non-aggressive basal cell carcinomas: A non-sponsored, randomized, prospective and double-blinded trial

Abstract Background In the photodynamic therapy (PDT) of non-aggressive basal cell carcinomas (BCCs), 5-aminolevulinic acid nanoemulsion (BF-200ALA) has shown non-inferior efficacy when compared with methyl aminolevulinate (MAL), a widely used photosensitizer. Hexyl aminolevulinate (HAL) is an interesting alternative photosensitizer. To our knowledge, this is the first study using HAL-PDT in the treatment of BCCs. Objectives To compare the histological clearance, tolerability (pain and post-treatment reaction), and cosmetic outcome of MAL, BF-200 ALA, and low-concentration HAL in the PDT of non-aggressive BCCs. Methods Ninety-eight histologically verified non-aggressive BCCs met the inclusion criteria, and 54 patients with 95 lesions completed the study. The lesions were randomized to receive LED-PDT in two repeated treatments with MAL, BF-200 ALA, or HAL. Efficacy was assessed both clinically and confirmed histologically at three months by blinded observers. Furthermore, cosmetic outcome, pain, post-treatment reactions fluorescence, and photobleaching were evaluated. Results According to intention-to-treat analyses, the histologically confirmed lesion clearance was 93.8% (95% confidence interval [CI] = 79.9?98.3) for MAL, 90.9% (95% CI = 76.4?96.9) for BF-200 ALA, and 87.9% (95% CI = 72.7?95.2) for HAL, with no differences between the arms (p=0.84). There were no differences between the arms as regards pain, post-treatment reactions, or cosmetic outcome. Conclusions PDT with low-concentration HAL and BF-200 ALA have a similar efficacy, tolerability, and cosmetic outcome compared to MAL. HAL is an interesting new option in dermatological PDT, since good efficacy is achieved with a low concentration.Peer reviewe

Type C botulism due to toxic feed affecting 52,000 farmed foxes and minks in Finland

The largest reported outbreak of type C botulism in fur production animals is described. Epidemiological investigation of 117 out of 157 (response rate, 74.5%) farms revealed that 44,130 animals died or were euthanized, while 8,033 animals with milder symptoms recovered. The overall death rate in all animals at risk was 21.7%. The death rates were significantly higher in blue and shadow foxes (24.2 and 27.8%, respectively) than in silver and blue silver foxes and minks (below 4%). All minks had been immunized against botulinum toxin type C. Deaths were associated with feed manufactured by a local processor, 83 of whose customer farms (70.9%) reported dead or sick animals. Five feedlots out of 19 delivered to the farms on the day preceding the onset of the outbreak (day 2) were associated with a death rate higher than 40%. These feedlots consisted of fresh feed processed on day 2 and feed processed 1 day earlier (day 1). In laboratory analysis, the day 2 feed contained botulinum toxin type C (>600 minimum lethal doses/g), while the day 1 feed did not contain toxin. Toxin was not detected in feed raw-material samples. Clostridium botulinum type C was detected by PCR in some feed components and in feed. However, as the feed temperature was continuously 8°C or below and the pH was continuously 5.6 or below according to the manufacturer, it seems unlikely that spore germination and toxin formation occurred during overnight storage. Hence, the events leading to toxin formation were not determined

Improved Classification of Alzheimer's Disease Data via Removal of Nuisance Variability

Diagnosis of Alzheimer's disease is based on the results of neuropsychological tests and available supporting biomarkers such as the results of imaging studies. The results of the tests and the values of biomarkers are dependent on the nuisance features, such as age and gender. In order to improve diagnostic power, the effects of the nuisance features have to be removed from the data. In this paper, four types of interactions between classification features and nuisance features were identified. Three methods were tested to remove these interactions from the classification data. In stratified analysis, a homogeneous subgroup was generated from a training set. Data correction method utilized linear regression model to remove the effects of nuisance features from data. The third method was a combination of these two methods. The methods were tested using all the baseline data from the Alzheimer's Disease Neuroimaging Initiative database in two classification studies: classifying control subjects from Alzheimer's disease patients and discriminating stable and progressive mild cognitive impairment subjects. The results show that both stratified analysis and data correction are able to statistically significantly improve the classification accuracy of several neuropsychological tests and imaging biomarkers. The improvements were especially large for the classification of stable and progressive mild cognitive impairment subjects, where the best improvements observed were 6% units. The data correction method gave better results for imaging biomarkers, whereas stratified analysis worked well with the neuropsychological tests. In conclusion, the study shows that the excess variability caused by nuisance features should be removed from the data to improve the classification accuracy, and therefore, the reliability of diagnosis making

A bootstrap approach for assessing the uncertainty of outcome probabilities when using a scoring system

Background: Scoring systems are a very attractive family of clinical predictive models, because the patient score can be calculated without using any data processing system. Their weakness lies in the difficulty of associating a reliable prognostic probability with each score. In this study a bootstrap approach for estimating confidence intervals of outcome probabilities is described and applied to design and optimize the performance of a scoring system for morbidity in intensive care units after heart surgery. Methods: The bias-corrected and accelerated bootstrap method was used to estimate the 95% confidence intervals of outcome probabilities associated with a scoring system. These confidence intervals were calculated for each score and each step of the scoring-system design by means of one thousand bootstrapped samples. 1090 consecutive adult patients who underwent coronary artery bypass graft were assigned at random to two groups of equal size, so as to define random training and testing sets with equal percentage morbidities. A collection of 78 preoperative, intraoperative and postoperative variables were considered as likely morbidity predictors. Results: Several competing scoring systems were compared on the basis of discrimination, generalization and uncertainty associated with the prognostic probabilities. The results showed that confidence intervals corresponding to different scores often overlapped, making it convenient to unite and thus reduce the score classes. After uniting two adjacent classes, a model with six score groups not only gave a satisfactory trade-off between discrimination and generalization, but also enabled patients to be allocated to classes, most of which were characterized by well separated confidence intervals of prognostic probabilities. Conclusions: Scoring systems are often designed solely on the basis of discrimination and generalization characteristics, to the detriment of prediction of a trustworthy outcome probability. The present example demonstrates that using a bootstrap method for the estimation of outcome-probability confidence intervals provides useful additional information about score-class statistics, guiding physicians towards the most convenient model for predicting morbidity outcomes in their clinical context

Epidural anesthesia and postoperative analgesia with ropivacaine and fentanyl in off-pump coronary artery bypass grafting: a randomized, controlled study

<p>Abstract</p> <p>Background</p> <p>Our aim was to assess the efficacy of thoracic epidural anesthesia (EA) followed by postoperative epidural infusion (EI) and patient-controlled epidural analgesia (PCEA) with ropivacaine/fentanyl in off-pump coronary artery bypass grafting (OPCAB).</p> <p>Methods</p> <p>In a prospective study, 93 patients were scheduled for OPCAB under propofol/fentanyl anesthesia and randomized to three postoperative analgesia regimens aiming at a visual analog scale (VAS) score < 30 mm at rest. The control group (n = 31) received intravenous fentanyl 10 μg/ml postoperatively 3-8 mL/h. After placement of an epidural catheter at the level of Th₂-Th₄before OPCAB, a thoracic EI group (n = 31) received EA intraoperatively with ropivacaine 0.75% 1 mg/kg and fentanyl 1 μg/kg followed by continuous EI of ropivacaine 0.2% 3-8 mL/h and fentanyl 2 μg/mL postoperatively. The PCEA group (n = 31), in addition to EA and EI, received PCEA (ropivacaine/fentanyl bolus 1 mL, lock-out interval 12 min) postoperatively. Hemodynamics and blood gases were measured throughout 24 h after OPCAB.</p> <p>Results</p> <p>During OPCAB, EA decreased arterial pressure transiently, counteracted changes in global ejection fraction and accumulation of extravascular lung water, and reduced the consumption of propofol by 15%, fentanyl by 50% and nitroglycerin by a 7-fold, but increased the requirements in colloids and vasopressors by 2- and 3-fold, respectively (<it>P </it>< 0.05). After OPCAB, PCEA increased PaO₂/FiO₂at 18 h and decreased the duration of mechanical ventilation by 32% compared with the control group (<it>P </it>< 0.05).</p> <p>Conclusions</p> <p>In OPCAB, EA with ropivacaine/fentanyl decreases arterial pressure transiently, optimizes myocardial performance and influences the perioperative fluid and vasoactive therapy. Postoperative EI combined with PCEA improves lung function and reduces time to extubation.</p> <p>Trial Registration</p> <p><a href="http://www.clinicaltrials.gov/ct2/show/NCT01384175">NCT01384175</a></p

Piecewise anomaly detection using minimal learning machine for hyperspectral images

Hyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are lowering. Anomaly detection is one of the popular remote sensing applications, which benefits from real-time solutions. A real-time solution has its limitations, for example, due to a large amount of hyperspectral data, platform’s (drones or a cube satellite) constraints on payload and processing capability. Other examples are the limitations of available energy and the complexity of the machine learning models. When anomalies are detected in real-time from the hyperspectral images, one crucial factor is to utilise a computationally efficient method. The Minimal Learning Machine is a distance-based classification algorithm, which can be modified for anomaly detection. Earlier studies confirms that the Minimal learning Machine (MLM) is capable of detecting efficiently global anomalies from the hyperspectral images with a false alarm rate of zero. In this study, we will show that by using a carefully selected lower threshold besides the higher threshold of the variance, it is possible to detect local and global anomalies with the MLM. The downside is that the improved method is highly sensitive with the respect to the noise. Thus, the second aim of this study is to improve the MLM’s robustness with respect to noise by introducing a novel approach, the piecewise MLM. With the new approach, the piecewise MLM can detect global and local anomalies, and the method is significantly more robust with respect to noise than the MLM. As a result, we have an interesting, easy to implement and computationally light method which is suitable for remote sensing applications.peerReviewe

Hyperspectral Imaging System in the Delineation of Ill-defined Basal Cell Carcinomas : A Pilot Study

Background Basal cell carcinoma (BCC) is the most common skin cancer in the Caucasian population. Eighty per cent of BCCs are located on the head and neck area. Clinically ill‐defined BCCs often represent histologically aggressive subtypes, and they can have subtle subclinical extensions leading to recurrence and the need for re‐excisions. Objectives The aim of this pilot study was to test the feasibility of a hyperspectral imaging system (HIS) in vivo in delineating the preoperatively lateral margins of ill‐defined BCCs on the head and neck area. Methods Ill‐defined BCCs were assessed clinically with a dermatoscope, photographed and imaged with HIS. This was followed by surgical procedures where the BCCs were excised at the clinical border and the marginal strip separately. HIS, with a 12‐cm2 field of view and fast data processing, records a hyperspectral graph for every pixel in the imaged area, thus creating a data cube. With automated computational modelling, the spectral data are converted into localization maps showing the tumour borders. Interpretation of these maps was compared to the histologically verified tumour borders. Results Sixteen BCCs were included. Of these cases, 10 of 16 were the aggressive subtype of BCC and 6 of 16 were nodular, superficial or a mixed type. HIS delineated the lesions more accurately in 12 of 16 of the BCCs compared to the clinical evaluation (4 of 16 wider and 8 of 16 smaller by HIS). In 2 of 16 cases, the HIS‐delineated lesion was wider without histopathological confirmation. In 2 of 16 cases, HIS did not detect the histopathologically confirmed subclinical extension. Conclusions HIS has the potential to be an easy and fast aid in the preoperative delineation of ill‐defined BCCs, but further adjustment and larger studies are warranted for an optimal outcome.peerReviewe

Ablative fractional laser‐assisted photodynamic therapy for lentigo maligna : a prospective pilot study

Background Lentigo maligna (LM) is an in‐situ form of melanoma carrying a risk of progression to invasive lentigo maligna melanoma (LMM). LM poses a clinical challenge, with subclinical extension and high recurrence rates after incomplete surgery. Alternative treatment methods have been investigated with varying results. Photodynamic therapy (PDT) with methylaminolaevulinate (MAL) has already proved promising in this respect. Objectives To investigate the efficacy of ablative fractional laser (AFL)‐assisted PDT with 5‐aminolaevulinic acid nanoemulsion (BF‐200 ALA) for treating LM. Methods In this non‐sponsored, prospective pilot study ten histologically verified LMs were treated with AFL‐assisted PDT three times at two week intervals using a light dose of 90 J/cm2 per treatment session. Local anaesthesia with ropivacain was used. Four weeks after the last PDT treatment the lesions were treated surgically with a wide excision and sent for histopathological examination. The primary outcome was complete histopathological clearance of the LM from the surgical specimen. Patient‐reported pain during illumination and the severity of the skin reaction after the PDT treatments were monitored as secondary outcomes. Results The complete histopathological clearance rate was 7 out of 10 LMs (70%). The pain during illumination was tolerable, with the mean pain scores for the PDT sessions on a visual assessment scale ranging from 2.9 to 3.8. Some severe skin reactions occurred during the treatment period, however. Conclusions AFL‐assisted PDT showed moderate efficacy in terms of histological clearance. It could constitute an alternative treatment for lentigo maligna but due to the side‐effects it should only be considered in inoperable cases.peerReviewe