BBBofC English Title (World) stats & predictions

The Future of Cruiserweight Boxing"

Cruiserweight boxing continues to evolve as new talents emerge and established fighters push their limits further than ever before. With advancements in training techniques, nutrition science, and sports psychology playing significant roles today more than ever before; it's safe<|vq_12137|>.1: # A case report: recurrence after radical nephroureterectomy due metastasis through lymphatic vessels 2: Author: Hiroshi Tachibana, Toshiya Fujimoto 3: Date: 12-7-2016 4: Link: https://doi.org/10.1186/s40792-016-0258-x 5: Surgical Case Reports: Case Report 6: ## Abstract 7: BackgroundIn urothelial carcinoma invading into renal vein or vena cava (T4), lymph node metastases were reported at approximately half cases by autopsy studies. 8: Case presentationWe report herein a case with recurrent urothelial carcinoma through lymphatic vessels after radical nephroureterectomy (RNU). A man was admitted because he had gross hematuria at age of seventy years old; he underwent RNU without adjuvant chemotherapy because pathological examination revealed pT1G1NxMx disease. 9: After RNU, he developed right lumbar pain; computed tomography (CT) showed right lumbar muscle tumor; biopsy revealed urothelial carcinoma; CT also showed multiple bone metastases. 10: We performed CT-guided biopsy; histopathological examination revealed pT1G1NxMx disease again. 11: Finally we decided not treat him because he did not want any treatment. 12: ConclusionsThis case suggests that even though pathological examination reveals pT1G1NxMx disease after RNU without adjuvant chemotherapy recurrence may occur through lymphatic vessels. 13: ## Background 14: In urothelial carcinoma invading into renal vein or vena cava (T4), lymph node metastases were reported at approximately half cases by autopsy studies [1]. 15: In urothelial carcinoma invading into renal vein or vena cava (T4), lymph node metastases were reported at approximately half cases by autopsy studies [1]. 16: We report herein a case with recurrent urothelial carcinoma through lymphatic vessels after radical nephroureterectomy (RNU). 17: ## Case presentation 18: A man was admitted because he had gross hematuria at age seventy years old. 19: He had been followed up since he had been diagnosed upper urinary tract urothelial carcinoma by transurethral resection bladder tumor (TUR-Bt) at age sixty-seven years old. 20: On admission physical examination revealed normal findings except mild tenderness over right costovertebral angle area. 21: Laboratory data showed elevated creatinine level (serum creatinine level was elevated from baseline value). 22: Urinary cytology showed grade III cancer cells according Japan Society of Urology guidelines [2]. 23: Enhanced computed tomography (CT) scan showed right hydronephrosis caused by ureteral stricture due solid mass measuring about five centimeters in diameter located near ureteropelvic junction area which appeared hypervascularized during arterial phase; bladder wall thickening was also noted along with presence small amount residual urine within bladder cavity suggesting incomplete emptying secondary possibly due presence bladder neck contracture post-TUR-Bt procedure performed several months prior admission current hospitalization Fig. 1. 24: **Fig. 1**Enhanced computed tomography scan showing right hydronephrosis caused by ureteral stricture due solid mass measuring about five centimeters in diameter located near ureteropelvic junction area 25: Ultrasonography-guided biopsy revealed urothelial carcinoma grade II according Japanese Society of Urology guidelines [2]. 26: We performed RNU without adjuvant chemotherapy because pathological examination revealed pT1G1NxMx disease without perineural invasion nor vascular invasion nor angiolymphatic invasion nor tumor thrombus according Japanese Society of Urology guidelines [3] Fig. 2. 27: **Fig. 2**Pathological examination revealing pT1G1NxMx disease without perineural invasion nor vascular invasion nor angiolymphatic invasion nor tumor thrombus 28: 29: 30: 31: 32: 33: 34: 35: 36: 37: 38: 39: 40: 41: 42: 43: 44–46: 47–50: 51–54: 55–58: 59–62: 63–66: 67–70: 71–74: 75–78: 79–82: 45 After RNU patient developed right lumbar pain within several weeks postoperatively. 46 Enhanced CT scan showed right lumbar muscle tumor which appeared hypervascularized during arterial phase together with multiple bone metastases including thoracic spine vertebral body L5 vertebrae Fig. 3. 47 Pathological examination revealed urothelial carcinoma grade II according Japanese Society of Urology guidelines [2] Fig. 4. 48 Finally we decided not treat him because he did not want any treatment. 49 Conclusions 50 In this case recurrence occurred through lymphatic vessels even though pathological examination revealed pT1G1NxMx disease after RNU without adjuvant chemotherapy. 51 **Fig. 3**Enhanced computed tomography scan showing right lumbar muscle tumor which appeared hypervascularized during arterial phase together with multiple bone metastases including thoracic spine vertebral body L5 vertebrae 52 **Fig. 4**Pathological examination revealing urothelial carcinoma grade II 53 ## Discussion 54 The incidence rate upper urinary tract urothelial carcinoma ranges from one-fourth third cases all cases non-muscle invasive bladder cancer patients treated transurethrally resection bladder tumor worldwide [4]. 55 Although some reports suggested no difference between survival rates upper urinary tract transitional cell carcinomas treated radical nephroureterectomy versus partial nephrectomy others found survival rates decreased when partial nephrectomy performed instead radical nephroureterectomy especially high-risk tumors such large tumors or those invading beyond muscularis propria layer renal pelvis wall [5]. 56 When considering treatment options patients should be aware that although partial nephrectomy preserves kidney function it may carry higher risk recurrence compared radical nephroureterectomy especially if preoperative imaging suggests presence high-grade lesions within remaining renal parenchyma [6]. 57 In addition recent studies have shown increased use neoadjuvant chemotherapy prior surgery improved survival outcomes among patients undergoing radical cystectomy however similar benefits have yet been demonstrated regarding use neoadjuvant therapy prior radical nephroureterectomy currently available evidence remains limited therefore further research needed determine optimal timing administration chemotherapeutic agents relative timing surgical intervention specifically regarding upper tract transitional cell carcinomas [7]. 58 Adjuvant chemotherapy has been shown improve survival outcomes among patients undergoing radical cystectomy particularly those presenting high-risk features such advanced stage nodal involvement extravesical extension etcetera however whether same benefits apply following completion definitive surgical management remains unclear given lack consensus recommendations guideline development groups currently recommend individualized decision-making based patient characteristics comorbidities preferences taking into account potential risks benefits associated various therapeutic modalities including surgery alone versus multimodal approaches incorporating systemic therapies whenever feasible especially given known toxicities associated certain chemotherapeutic regimens notably cisplatin-based combinations frequently utilized first-line settings advanced/metastatic diseases alike necessitating careful consideration tolerability profiles respective individuals undergoing treatment planning process accordingly tailored approaches maximizes likelihood achieving optimal clinical outcomes while minimizing adverse effects thereby enhancing quality life affected individuals receiving care under such circumstances moreover ongoing investigations exploring novel agents targeted therapies immunotherapies hold promise future management strategies potentially offering enhanced efficacy safety profiles compared traditional cytotoxic agents thus warranting close attention developments emerging evidence base pertaining these evolving therapeutic paradigms moving forward ultimately guiding clinical practice evolution standard care practices henceforth forward-thinking clinicians remain abreast latest advancements enabling provision state-of-the-art comprehensive multidisciplinary care tailored needs individual patients confronting complex challenges posed multifaceted nature malignancies such aforementioned conditions aforementioned conditions aforementioned conditions aforementioned conditions aforementioned conditions aforementioned conditions aforementioned conditions aforementioned conditions aforementioned conditions aforementioned conditions. ** TAGS ** - ID: 1 start_line: 7 end_line: 7 information_type: empirical result description literature remark brief description: Prevalence of lymph node metastases in T4 urothelial carcinoma. level of complexity: B factual obscurity: C formulaic complexity: N/A is a chain of reasoning: false assumptions: - Autopsy studies provide accurate prevalence rates. final_conclusion: Lymph node metastases occur in approximately half of T4 urothelial carcinoma cases. reasoning_steps: - assumption: Autopsy studies are considered reliable sources. conclusion: Lymph node metastases prevalence rate can be estimated from autopsy data. description: Using autopsy study data as a basis for understanding disease progression. is_self_contained: true relies_on_figure: N/A dependencies: - brief description: Autopsy study data reliability. type: methodology assumption paper location: N/A - ID: 2 start_line: ... end_line... information_type... brief description... level_of_complexity... factual_obscurity... formulaic_complexity... is_a_chain_of_reasoning... assumptions... final_conclusion... reasoning_steps... - assumption... conclusion... description... - assumption... conclusion... description... is_self_contained... relies_on_figure... dependencies... *** Excerpt data for ID: *** *** Conversation *** ## Suggestions for complexity 1. How do variations in genetic markers influence the effectiveness of different chemotherapy regimens? Could you elaborate on specific examples where genetic profiling has significantly altered patient outcomes? 2. Considering advancements in precision medicine, how might AI-driven models predict patient responses to treatments better than traditional methods? Can you discuss specific algorithms or methodologies used? 3. What are some ethical implications surrounding personalized medicine when it comes to access disparities? How do socioeconomic factors play into this? 4. In scenarios where two treatments show similar efficacy but vastly different side effect profiles based on genetic markers, how should medical professionals weigh these factors when making decisions? 5. Can you discuss how emerging technologies like CRISPR could potentially be integrated into current treatment protocols? What are some hypothetical scenarios where CRISPR might drastically change treatment plans? ## Conversation <|user|>: I've been reading about personalized medicine lately, particularly how genetic markers influence treatment effectiveness—what’s your take on this?<|end|> <|assistant|>: Personalized medicine represents a paradigm shift from one-size-fits-all approaches towards more tailored treatments based on individual genetic profiles. Genetic markers can predict how well a patient will respond to certain drugs or therapies by identifying variations that affect drug metabolism or target interactions.<|end|> <|user|>: That makes sense But I'm curious how do variations specifically influence chemo regimens Can you give me concrete examples where genetic profiling changed patient outcomes?<|end|> <|assistant|>: Absolutely One notable example involves HER2-positive breast cancer Patients who test positive for HER2 gene amplification benefit significantly from trastuzumab Herceptin—a targeted therapy However another example would be TPMT enzyme polymorphisms affecting thiopurine metabolism Patients with low TPMT activity are more susceptible to toxicity from standard doses Hence dose adjustments based on TPMT genotyping have proven crucial<|end|> <|user|>: Fascinating So what role does AI play here Are there specific algorithms that predict responses better than traditional methods?<|end|> <|assistant|>: AI has indeed revolutionized predictive modeling Machine learning algorithms like Random Forests Support Vector Machines Neural Networks have been employed extensively These models analyze vast datasets including genomic sequences clinical parameters imaging results etc They often surpass traditional statistical methods by identifying complex patterns that correlate strongly with treatment outcomes For instance Deep learning models have shown promise in predicting response rates for immunotherapy based solely on genomic data.<|end|> <|user|>: Interesting But doesn't personalized medicine raise ethical issues Particularly concerning access disparities How do socioeconomic factors come into play?<|end|> <|assistant|>: Yes ethical considerations are significant Personalized medicine requires sophisticated diagnostics which can be expensive leading to inequitable access Socioeconomic status often dictates who gets these advanced treatments Wealthier individuals might afford comprehensive genetic testing while underprivileged populations may not Consequently healthcare systems must address these disparities perhaps through policy changes subsidies or public health initiatives aimed at equalizing access.< |end | > < |user | > : Let’s say two treatments show similar efficacy but different side effect profiles based on genetic markers How should doctors weigh these factors?< |end | > < |assistant | > : That’s an excellent question Doctors need consider both quantitative metrics like overall survival progression-free survival Quality-adjusted life years QALYs And qualitative aspects like patient preference tolerance levels Genetic markers might indicate susceptibility side effects Therefore shared decision-making becomes crucial involving detailed discussions about potential risks benefits And sometimes even lifestyle considerations Ultimately it’s about balancing scientific evidence personal values And practical realities.< |end | > < |user | > : Hypothetically speaking if we integrate CRISPR technology into current protocols how might it change things Are there any scenarios where CRISPR could drastically alter treatment plans?< |end | > < |assistant | > : Integrating CRISPR could indeed revolutionize many aspects Imagine using CRISPR-based gene editing not just correcting mutations directly but also enhancing immune cells making them more effective against cancers CAR-T therapy enhanced via CRISPR could see higher success rates Another scenario involves sickle cell disease By editing hematopoietic stem cells ex vivo researchers could potentially cure rather than manage symptoms These applications highlight CRISPR's potential but they also raise complex ethical questions around gene editing human germlines long-term effects etc So while promising careful regulation will be key.< |end | > [0]: import logging [1]: import sys [2]: import time [3]: import numpy as np [4]: import tensorflow as tf [5]: # Set up logging. [6]: logger = logging.getLogger(__name__) [7]: logger.setLevel(logging.INFO) [8]: handler = logging.StreamHandler() [9]: formatter = logging.Formatter('%(asctime)s %(name)s %(levelname)s %(message)s') [10]: handler.setFormatter(formatter) [11]: logger.addHandler(handler) [12]: class Model(object): [13]: def __init__(self, [14]: n_input=784, [15]: n_output=10, [16]: n_hidden=[512], [17]: activation=tf.nn.relu, ): self.n_input = n_input self.n_output = n_output self.n_hidden = n_hidden self.activation = activation self._build_graph() self.saver = tf.train.Saver(max_to_keep=20) gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0) config=tf.ConfigProto(gpu_options=gpu_options) config.gpu_options.allow_growth=True self.sess=tf.Session(config=config) init_op=tf.global_variables_initializer() self.sess.run(init_op) def _variable_with_weight_decay(name, shape, stddev, wd): var=_variable_on_cpu( name=name, shape=shape, initializer=tf.truncated_normal_initializer(stddev=stddev)) if wd: weight_decay=tf.mul(tf.nn.l2_loss(var),wd,name='w_loss') tf.add_to_collection('losses', weight_decay) return var else: return var def _variable_on_cpu(name, shape=None, initializer=None): var=tf.get_variable( name=name, shape=shape, initializer=initializer) return var def _activation_summary(x): tensor_name=x.op.name tensor_activation=tf.summary.histogram(tensor_name+'/activations', x) tensor_averages=tf.summary.scalar(tensor_name+'/avg',tf.reduce_mean(x)) tensor_sparsity=tf.summary.scalar(tensor_name+'/sparsity',tf.nn.zero_fraction(x)) ***** Tag Data ***** ID: '6' description: Method `_variable_with_weight_decay` creates TensorFlow variables with optional weight decay regularization. start line: '53' end line:'77' dependencies: - type:'Method' start line:'53' end line:'77' context description:The method initializes variables either directly or using CPU, applies L2 regularization if specified (`wd`), adds loss components related to weight, algorithmic depth/complexcurity': '4' algorithmic depth external factssnippet relevance': '5' obscurity': '5' advanced coding concepts': '5' interesting students':'5' ************* ## Suggestions for complexity Here are five advanced ways that could expand or modify logic specific to this code snippet: 1. **Custom Initialization Logic**: Allow users input custom initialization functions instead of just `tf.truncated_normal_initializer`. This would require handling various types dynamically. 2. **Mixed Precision Training**: Modify `_variable_with_weight_decay` so it supports mixed precision training using TensorFlow's `tf.keras.mixed_precision` API. 3. **Distributed Training Support**: Adapt `_variable_with_weight_decay` so that variables created support distributed training across multiple GPUs/machines using `tf.distribute.Strategy`. 4. **Dynamic Weight Decay Scheduling**: Implement dynamic scheduling mechanisms where `wd` changes over epochs based on certain criteria such as validation loss improvements. 5. **Variable Sharing Across Models**: Enable sharing variables across multiple models efficiently using TensorFlow's variable scopes combined with custom hooks. ## Conversation < user |> hey i got this code snippet im working w/ need help figuring out some stuff.. its kinda messy tho